Tesis: "Diagnostics and prognostics"

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Rosen, Charles Michael. "Demonstration : integrated diagnostics/prognostics for condition-based maintenance". Thesis, Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/18954.

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Tai, Zhongtian. "Aircraft electrical power system diagnostics, prognostics and health management". Thesis, Cranfield University, 2009. http://dspace.lib.cranfield.ac.uk/handle/1826/9593.

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In recent years, the loads needing electrical power in military aircraft and civil jet keep increasing, this put huge pressure on the electrical power system (EPS). As EPS becomes more powerful and complex, its reliability and maintenance becomes difficult problems to designers, manufacturers and customers. To improve the mission reliability and reduce life cycle cost, the EPS needs health management. This thesis developed a set of generic health management methods for the EPS, which can monitor system status; diagnose faults/failures in component level correctly and predict impending faults/failures exactly and predict remaining useful life of critical components precisely. The writer compared a few diagnostic and prognostic approaches in detail, and then found suitable ones for EPS. Then the major components and key parameters needed to be monitored are obtained, after function hazard analysis and failure modes effects analysis of EPS. A diagnostic process is applied to EPS using Dynamic Case-based Reasoning approach, whilst hybrid prognostic methods are suggested to the system. After that, Diagnostic, Prognostic and Health Management architecture of EPS is built up in system level based on diagnostic and prognostic process. Finally, qualitative evaluations of DPHM explain given. This research is an extension of group design project (GDP) work, the GDP report is arranged in the Appendix A.

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3

Tamilselvan, Prasannavenkatesh. "Advanced failure diagnostics and prognostics for complex system health management". Diss., Wichita State University, 2014. http://hdl.handle.net/10057/10942.

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This dissertation consists of four published or accepted journal articles that address some of the key problems in prognostics and health management area (PHM). Effective health diagnostics and prognostics provide multifarious benefits such as improved safety, improved reliability and reduced costs for operation and maintenance (O&M) of complex engineered systems. Extensive literature reviews on PHM for diagnostics of system health conditions and O&M decision-making for complex engineered systems have identified important challenge problems for this dissertation as follows: - Effective diagnostics of current health states based on heterogeneous sensory data from multiple sensors is an intricate problem for condition monitoring techniques to be applied on complex engineered systems, mainly due to high system complexity and sensory data heterogeneity; - With an increasing system complexity, it is extraordinarily difficult to have a well-tested system so that all potential faulty states can be realized and studied at product testing stage. Thus, real time health diagnostics requires automatic detection of unexampled system faulty states based upon sensory data to avoid sudden catastrophic system failures; - Despite successful applications of different diagnostic algorithms in various engineering fields, a challenge for health diagnostics is that an implicit relationship between different system health states and features of sensory signals makes it difficult to develop a generally applicable health diagnostics technique. - Although diagnostics and prognostics can provide valuable information for proactive actions in preventing system failures, their benefits have not been fully utilized for the O&M decision-making process. To carefully address these important research problems, this dissertation proposes four research solutions: a multi-sensor health diagnostics technique using deep belief network, a tri-fold hybrid classification approach for diagnostics with unexampled faulty states, a multi-attribute classification fusion technique to develop a generally applicable health diagnostics framework and a generic prognostics-informed O&M decision-making framework by utilizing failure prediction information in the O&M decision-making process. In this dissertation, different practical engineering applications will be employed as case studies to demonstrate the efficacy of proposed research solutions.
Thesis (Ph.D.)-- Wichita State University, College of Engineering, Dept. of Industrial and Manufacturing Engineering

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4

Macmann, Owen. "Performing Diagnostics & Prognostics On Simulated Engine Failures Using Neural Networks". University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1461593737.

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5

Marx, Douw. "Towards a hybrid approach for diagnostics and prognostics of planetary gearboxes". Diss., University of Pretoria, 2021. http://hdl.handle.net/2263/78157.

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The reliable operation of planetary gearboxes is critical for the sustained operation of many machines such as wind turbines and helicopter transmissions. Hybrid methods that make use of the respective advantages of physics-based and data-driven models can be valuable in addressing the unique challenges associated with the condition monitoring of planetary gearboxes. In this dissertation, a hybrid framework for diagnostics and prognostics of planetary gearboxes is proposed. The proposed framework aims to diagnose and predict the root crack length in a planet gear tooth from accelerometer measurements. Physics-based and data-driven models are combined to exploit their respective advantages, and it is assumed that no failure data is available for training these models. Components required for the implementation of the proposed framework are studied separately and challenges associated with each component are discussed. The proposed hybrid framework comprises a health state estimation and health state prediction part. In the health state estimation part of the proposed framework, the crack length is diagnosed from the measured vibration response. To do this, the following model components are implemented: A first finite element model is used to simulate the crack growth path in the planet gear tooth. Thereafter, a second finite element model is used to establish a relationship between the gearbox time varying mesh stiffness, and the crack length in the planet gear tooth. A lumped mass model is then used to model the vibration response of the gearbox housing subject to the gearbox time varying mesh stiffness excitation. The measurements from an accelerometer mounted on the gearbox housing are processed by computing the synchronous average. Finally, these model components are combined with an additional data-driven model for diagnosing the crack length from the measured vibration response through the solution of an inverse problem. After the crack length is diagnosed through the health state estimation model, the Paris crack propagation law and Bayesian state estimation techniques are used to predict the remaining useful life of the gearbox. To validate the proposed hybrid framework, an experimental setup is developed. The experimental setup allows for the measurement of the vibration response of a planetary gearbox with different tooth root crack lengths in the planet gear. However, challenges in reliably detecting the damage in the experimental setup lead to the use of simulated data for studying the respective components of the hybrid method. Studies conducted using simulated data highlighted interesting challenges that need to be overcome before a hybrid diagnostics and prognostics framework for planetary gearboxes can be applied in practice.
Dissertation (MSc)--University of Pretoria, 2021.
Eskom EPPEI
Mechanical and Aeronautical Engineering
Msc
Unrestricted

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Van, Dyke Jason. "Modeling Behaviour of Damaged Turbine Blades for Engine Health Diagnostics and Prognostics". Thèse, Université d'Ottawa / University of Ottawa, 2011. http://hdl.handle.net/10393/20312.

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The reliability of modern gas turbine engines is largely due to careful damage tolerant design a method of structural design based on the assumption that flaws (cracks) exist in any structure and will continue to grow with usage. With proper monitoring, largely in the form of periodic inspections at conservative intervals reliability and safety is maintained. These methods while reliable can lead to the early retirement of some components and unforeseen failure if design assumptions fail to reflect reality. With improvements to sensor and computing technology there is a growing interest in a system that could continuously monitor the health of structural aircraft as well as forecast future damage accumulation in real-time. Through the use of two-dimensional and three-dimensional numerical modeling the initial goals and findings for this continued work include: (a) establishing measurable parameters directly linked to the health of the blade and (b) the feasibility of detecting accumulated damage to the structural material and thermal barrier coating as well as the onset of damage causing structural failure.

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7

SOAVE, Elia. "Diagnostics and prognostics of rotating machines through cyclostationary methods and machine learning". Doctoral thesis, Università degli studi di Ferrara, 2022. http://hdl.handle.net/11392/2490999.

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In the last decades, the vibration analysis has been exploited for monitoring many mechanical systems for industrial applications. Although several works demonstrated how the vibration based diagnostics may reach satisfactory results, the nowadays industrial scenario is deeply changing, driven by the fundamental need of time and cost reduction. In this direction, the academic research has to focus on the improvement of the computational efficiency for the signal processing techniques applied in the mechanical diagnostics field. In the same way, the industrial word requires an increasing attention to the predictive maintenance for estimating the system failure avoiding unnecessary machine downtimes for maintenance operations. In this contest, in the recent years the research activity has been moved to the development of prognostic models for the prediction of the remaining useful life. However, it is important to keep in mind how the two fields are strictly connected, being the diagnostics the base on which build the effectiveness of each prognostic model. On these grounds, this thesis has been focused on these two different but linked areas for the detection and prediction of possible failures inside rotating machines in the industrial framework. The first part of the thesis focuses on the development of a blind deconvolution indicator based on the cyclostationary theory for the fault identification in rotating machines. The novel criterion aims to decrease the computational cost of the blind deconvolution through the exploitation of the Fourier-Bessel series expansion due to its modulated nature more comparable with the fault related vibration pattern. The proposed indicator is extensively compared to the other cyclostationary one based on the classic Fourier transform, taking into account both synthesized and real vibration signals. The comparison proves the improvement given by the proposed criterion in terms of number of operations required by the blind deconvolution algorithm as well as its diagnostic capability also for noisy measured signals. The originality of this part regards the combination of cyclostationarity and Fourier-Bessel transform that leads to the definition of a novel blind deconvolution criterion that keeps the diagnostic effectiveness of cyclostationarity reducing the computational cost in order to meet the industrial requirements. The second part regards the definition of a novel prognostic model from the family of the hidden Markov models constructed on a generalized Gaussian distribution. The target of the proposed method is a better fitting quality of the data distribution in the last damaging phase. In fact, the fault appearance and evolution reflects on a modification of the observation distribution within the states and consequently a generalized density function allows the changes on the distribution form through the values of some model parameters. The proposed method is compared in terms of fitting quality and state sequence prediction to the classic Gaussian based hidden Markov model through the analysis of several run to failure tests performed on rolling element bearings and more complex systems. The novelty of this part regards the definition of a new iterative algorithm for the estimation of the generalized Gaussian model parameters starting from the observations on the physical system for both monovariate and multivariate distributions. Furthermore, the strictly connection between diagnostics and prognostics is demonstrated through the analysis of a not monotonically increasing damaging process proving how the selection of a suitable indicator enables the correct health state estimation.
Negli ultimi decenni, l’analisi vibrazionale è stata sfruttata per il monitoraggio di molti sistemi meccanici per applicazioni industriali. Nonostante molte pubblicazioni abbiano dimostrato come la diagnostica vibrazionale possa raggiungere risultati soddisfacenti, lo scenario industriale odierno è in profondo cambiamento, guidato dalla necessità di ridurre tempi e costi produttivi. In questa direzione, la ricerca deve concentrarsi sul miglioramento dell’efficienza computazionale delle tecniche di analisi del segnale applicate a fini diagnostici. Allo stesso modo, il mondo industriale richiede una sempre maggior attenzione per la manutenzione predittiva, al fine di stimare l’effettivo danneggiamento del sistema evitando così inutili fermi macchina per operazioni manutentive. In tale ambito, negli ultimi anni l’attività di ricerca si sta spostando verso lo sviluppo di modelli prognostici finalizzati alla stima della vita utile residua dei componenti. Tuttavia, è importante ricordare come i due ambiti siano strettamente connessi, essendo la diagnostica la base su cui fondare l’efficacia di ciascun modello prognostico. Su questa base, questa tesi è stata incentrata su queste due diverse, ma tra loro connesse, aree al fine di identificare e predire possibile cause di cedimento su macchine rotanti per applicazioni industriali. La prima parte della tesi è concentrata sullo sviluppo di un nuovo indicatore di blind deconvolution per l’identificazione di difetti su organi rotanti sulla base della teoria ciclostazionaria. Il criterio presentato vuole andare a ridurre il costo computazionale richiesto dalla blind deconvolution tramite l’utilizzo della serie di Fourier-Bessel grazie alla sua natura modulata, maggiormente affine alla tipica firma vibratoria del difetto. L’indicatore proposto viene accuratamente confrontato con il suo analogo basato sulla classica serie di Fourier considerando sia segnali simulati che segnali di vibrazione reali. Il confronto vuole dimostrare il miglioramento fornito dal nuovo criterio in termini sia di minor numero di operazioni richieste dall’algoritmo che di efficacia diagnostica anche in condizioni di segnale molto rumoroso. Il contributo innovativo di questa parte riguarda la combinazione di ciclostazionarietà e serie di Furier-Bessel che porta alla definizione di un nuovo criterio di blind deconvolution in grado di mantenere l’efficacia diagnostica della ciclostazionarietà ma con un minor tempo computazionale per venire incontro alle richieste del mondo industriale. La second parte riguarda la definizione di un nuovo modello prognostico, appartenente alla famiglia degli hidden Markov models, costruito partendo da una distribuzione Gaussiana generalizzata. L’obbiettivo del metodo proposto è una miglior riproduzione della reale distribuzione dei dati, in particolar modo negli ultimi stadi del danneggiamento. Infatti, la comparsa e l’evoluzione del difetto comporta una modifica della distribuzione delle osservazioni fra i diversi stati. Di conseguenza, una densità di probabilità generalizzata permette la modificazione della forma della distribuzione tramite diversi valori dei parametri del modello. Il metodo proposto viene confrontato con il classico hidden Markov model di base Gaussiana in termini di qualità di riproduzione della distribuzione e predizione della sequenza di stati tramite l’analisi di alcuni test di rottura su cuscinetti volventi e sistemi complessi. L’innovatività di questa parte è data dalla definizione di un algoritmo iterativo per la stima dei parametri del modello nell’ipotesi di distribuzione Gaussiana generalizzata, sia nel caso monovariato che multivariato, partendo dalle osservazioni sul sistema fisico in esame.

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8

Barlas, Irtaza. "A Multiagent Framework for a Diagnostic and Prognostic System". Diss., Georgia Institute of Technology, 2003. http://hdl.handle.net/1853/5290.

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A Multiagent Framework for a Diagnostic and Prognostic System Irtaza Barlas 124 Pages Directed By: Dr. George Vactsevanos The shortcomings of the current diagnostic and prognostic systems stem from the limitations of their frameworks. The framework is typically designed on the passive, open loop, static, and isolated notions of diagnostics, in that the framework does not observe its diagnostic results (open-looped), hence can not improve its performance (static). Its passivity is attributed to the fact that an external event triggers the diagnostic or prognostic action. There is also no effort in place to team-up the diagnostic systems for a collective learning, hence the implementation is isolated. In this research we extend the current approaches of the design and implementation of diagnostic and prognostic systems by presenting a framework based upon Multiagent systems. This research created novel architectures by providing such unique features to the framework, as learning, reasoning, and coordination. As the primary focus of the research the concept of Case-Based Reasoning was exploited to reason in the temporal domain to generate better prognosis, and improve the accuracy of detection as well as prediction. It was shown that the dynamic behavior of the intelligent agent helps it to learn over time, resulting in improved performance. An analysis is presented to show that a coordinated effort to diagnose also makes sense in uncertain situations when there are certain number of systems attempting to communicate certain number of failures, since there can be high probability of finding a shareable experience.

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9

Brahimi, Mehdi. "Développement d'une approche de 'Prognostics and Health Management' pour l'infrastructure ferroviaire". Electronic Thesis or Diss., Bourgogne Franche-Comté, 2018. http://www.theses.fr/2018UBFCD026.

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Le développement de nouveaux systèmes intelligents qui permettent de répondre aux besoins croissants en matière de transport et de disponibilité est un enjeu clé de compétitivité pour les différents acteurs de l’industrie ferroviaire. Dans ce contexte, le système captage de courant composé de la caténaire et du pantographe est un élément clé de l'infrastructure ferroviaire. En effet, un composent abîmé ou dégradé de la caténaire peut entraîner d'importants retards, endommager l'infrastructure et le matériel roulant, et engendrer d'importantes pertes financières pour l'exploitant ferroviaire. Dans ce cadre, des constructeurs ferroviaires tels que Alstom tentent de développer des solutions de maintenance modernes afin de gérer l’opérabilité des systèmes et d’assurer leurs disponibilités. Afin d’atteindre des objectifs de disponibilité, de fiabilité et de sécurité des systèmes, la démarche la plus étudiée actuellement est le « Prognostics and Health Management » (PHM). Dans cette thèse, la première contribution consiste à formaliser un processus de déploiement et de développement d’un système PHM adapté au contexte de l’infrastructure ferroviaire et plus particulièrement au système de captage de courant. La deuxième contribution de la thèse porte sur le diagnostic du système caténaire. La procédure de diagnostic proposée permet de détecter, d’identifier et de localiser différents modes de défaillance de la caténaire à partir des mesures de la force de contact. L’approche considérée est basée sur des machines à vecteurs de supports et la définition de descripteurs extraits de la force de contact. Les données utilisées pour la validation du diagnostic sont issus de simulation dans un premier temps, par la suite des essais en ligne ont permis de valider la méthode et de proposer une approche pour la mise en œuvre industrielle du diagnostic. Enfin, la dernière contribution concerne le développement d’une fonction de pronostic pour le fil de contact de la caténaire. Cette méthode est basée sur une l’utilisation de modèles physique d’usure du fil de contact et l’utilisation d’une approche de pronostic par filtre. Les performances de pronostic ont été évaluées en fonction de la pertinence de la décision de maintenance induite par le pronostic. Cette thèse a permis la mise en place de différentes approches pour le déploiement d’un système PHM pour la caténaire
Developing intelligent systems that can meet the growing needs for transportation is a key competitiveness issue for the different stakeholders in the railway industry. In this context, the current collection system, consisting of the overhead contact line (catenary) and the pantograph, is a key element of the railway infrastructure. In fact, a damaged or degraded component of the catenary can cause significant delays, can damage the infrastructure and the rolling stock, and can cause significant financial losses for the railway operator. In this way, railway manufacturers such as Alstom are trying to develop modern maintenance solutions to manage the operability of systems and ensure their availability. In order to achieve objectives of system availability, reliability, and safety, the most currently studied approach is the "Prognostics and Health Management" (PHM). In this thesis, the first contribution consists in formalizing a process for the deployment and development of a PHM system regarding the specific context of the railway infrastructure, and more particularly the current collection system. The second contribution of the thesis deals with the diagnostics function for the overhead contact line system. The proposed diagnostics approach ensures the detection, the identification, and the localization of different failure modes of the catenary from contact force measurements. The considered approach is based on support vector machines (SVM) and specific features extracted from the contact force. The data used for the validation of the diagnostics procedure are derived from the simulation, afterward, inline data are used to validate the method and to propose an industrial deployment of the diagnostics approach. Finally, the last contribution concerns the development of a prognostics function for the catenary contact wire. This method is based on the use of wear models and filtering approaches. Prognostics performances were evaluated based on the relevance of the prognostics-based maintenance decision. This thesis allowed the implementation of different approaches for a PHM deployment for the catenary system

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10

Yang, Yang. "Aircraft landing gear extension and retraction control system diagnostics, prognostics and health management". Thesis, Cranfield University, 2012. http://dspace.lib.cranfield.ac.uk/handle/1826/7266.

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This thesis contains the Group Design Project (GDP) work and Individual Research Project (IRP) work. The target of this GDP was to design a long range flying wing passenger aircraft to meet the increasing global aircraft demand. The name of this flying wing aircraft is FW-11. This is a project cooperated between Aviation Industry Corporation of China (AVIC) and Cranfield University. The writer was involved in the conceptual design stage of this project. The author was in charge of the engine market, engine selection, engine sizing and performance. The target of the IRP is to build a set of health management methods including system real-time monitoring, accurate fault diagnosis and prognosis of major components which are suitable for the aircraft landing gear extension and retraction control system. These technologies have the capability to improve mission reliability of the aircraft and the maintenance costs could be reduced. Simultaneously, aircraft landing gear extension and retraction control system, as one of the most important aircraft systems on-board, could directly affect the flight safety. Consequently, diagnostic, prognostic and health management (DPHM) technology is necessary for the system. Based on the FHA, FMEA and FTA of the aircraft landing gear extension and retraction control system, each of the catastrophic events, all the root causes and their effects were identified. Synchronously, all the components which are related to the catastrophic events were found. The rule-based expert system diagnostic technology was chosen from the available approaches and it was successfully applied on the system. Appropriate prognosis approach was recommended for each component of the system according to the features of components of the system. Finally, the DPHM architecture of the landing gear extension and retraction control system was built.

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11

Rhen, Mats. "Studies of condition monitoring methods for system health assessment : health diagnostics and prognostics". Licentiate thesis, Luleå tekniska universitet, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-26751.

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Increasing interest in productivity, safety and environmental issues have highlighted the area of maintenance and reliability. The increasing cost of maintenance covers both preservation and sustainable exploitation of resources and awareness in maintaining equipment in a way to ensure return on investment both in the short and long run. The information obtained from condition monitoring of existing turbine, plant, rails and pumps can provide an important basis for dimensioning of future systems and components. The main objective of this research work is to develop and apply methods for efficient condition monitoring, and hence reduce maintenance costs and provide a framework for development and implementation of computer based decision tools. Furthermore, methods enabling existing process data and cost effective transducers to be used together with modern data analysis and diagnostic tools for condition monitoring of complex mechanical systems have been examined and prototypes developed. The areas of investigation covered in this work are hydropower turbines, rails and the main cooling pumps in a nuclear power-plant. The interest in diagnostics for hydropower turbines was driven by the obvious risk of contamination of water by oil leaks and expensive refurbishments caused by wear of the Kaplan turbine vane bearings. The intrest in risk analysis was motivated by Vattenfall's intrest in gaining knowledge about the state of all turbines in the company. The aim of this project was to develop a generic model of hydropower turbine behavior using physics-based models based on material properties, load tolerances, etc.. An important question was whether it was possible to predict the wear rate and plan predictive replacement or maintenance. A systematic approach to find failure modes, their effects, their causes and consequences in combination with Fault Tree Analysis was needed. The objective of this project was to examine a systematic approach to map failure modes and their causes in an hydropower turbine. We have restricted the study to turbine units of the Kaplan, Francis and tube types. The objective of the study concerning rail track was to develop methods and equipment for detection of surface damage in rail track rail in addition to the present system of practice of visual examination. The equipment developed has to be used to obtain objective statistical data for evaluating maintenance methods and efforts. We have restricted the study to spalling and headchecks on the rail head surface and running edge. The method developed enables measurements of different types of surface damage such as spalling and shelling to be made with inductive transducers sensitive to the distance to the measured object. The assumption here is that the damage being detected is characterized by the absence of material from rail surface. The main object of condition monitoring of the cooling pumps was to be able to detect bearing wear in order to be able to plan and carry out restoration well ahead of breakdown or bearing seizure. The study was restricted to the main cooling pump motor and its main bearings. Condition monitoring of the pumps was done using a method based on current measurements. Analysis of the currents on the main cooling pump of the power plant proved that it is possible to monitor the condition of the pump in spite of the presence of electronic frequency converters which distorts the signal.

Godkänd; 2002; 20070222 (ysko)

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12

Kim, Hack-Eun. "Machine prognostics based on health state probability estimation". Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/41739/1/Hack-Eun_Kim_Thesis.pdf.

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The ability to accurately predict the remaining useful life of machine components is critical for machine continuous operation and can also improve productivity and enhance system’s safety. In condition-based maintenance (CBM), maintenance is performed based on information collected through condition monitoring and assessment of the machine health. Effective diagnostics and prognostics are important aspects of CBM for maintenance engineers to schedule a repair and to acquire replacement components before the components actually fail. Although a variety of prognostic methodologies have been reported recently, their application in industry is still relatively new and mostly focused on the prediction of specific component degradations. Furthermore, they required significant and sufficient number of fault indicators to accurately prognose the component faults. Hence, sufficient usage of health indicators in prognostics for the effective interpretation of machine degradation process is still required. Major challenges for accurate longterm prediction of remaining useful life (RUL) still remain to be addressed. Therefore, continuous development and improvement of a machine health management system and accurate long-term prediction of machine remnant life is required in real industry application. This thesis presents an integrated diagnostics and prognostics framework based on health state probability estimation for accurate and long-term prediction of machine remnant life. In the proposed model, prior empirical (historical) knowledge is embedded in the integrated diagnostics and prognostics system for classification of impending faults in machine system and accurate probability estimation of discrete degradation stages (health states). The methodology assumes that machine degradation consists of a series of degraded states (health states) which effectively represent the dynamic and stochastic process of machine failure. The estimation of discrete health state probability for the prediction of machine remnant life is performed using the ability of classification algorithms. To employ the appropriate classifier for health state probability estimation in the proposed model, comparative intelligent diagnostic tests were conducted using five different classifiers applied to the progressive fault data of three different faults in a high pressure liquefied natural gas (HP-LNG) pump. As a result of this comparison study, SVMs were employed in heath state probability estimation for the prediction of machine failure in this research. The proposed prognostic methodology has been successfully tested and validated using a number of case studies from simulation tests to real industry applications. The results from two actual failure case studies using simulations and experiments indicate that accurate estimation of health states is achievable and the proposed method provides accurate long-term prediction of machine remnant life. In addition, the results of experimental tests show that the proposed model has the capability of providing early warning of abnormal machine operating conditions by identifying the transitional states of machine fault conditions. Finally, the proposed prognostic model is validated through two industrial case studies. The optimal number of health states which can minimise the model training error without significant decrease of prediction accuracy was also examined through several health states of bearing failure. The results were very encouraging and show that the proposed prognostic model based on health state probability estimation has the potential to be used as a generic and scalable asset health estimation tool in industrial machinery.

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Rezvanizaniani, Seyed Mohammad. "Probabilistic Based Classification Techniques for Improved Prognostics Using Time Series Data". University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1428048932.

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14

Bresolin, Silvia. "Advancements in Molecular Prognostics and Diagnostics of Pediatric Myelodysplastic Syndrome and Juvenile Myelomonocytic Leukemia". Doctoral thesis, Università degli studi di Padova, 2012. http://hdl.handle.net/11577/3422195.

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Myelodysplastic syndromes (MDS) and juvenile myelomonocytic leukemia (JMML) are rare hematopoietic stem cell diseases constituting less then 5% of hematopoietic neoplasia in childhood. Pediatric MDS is an heterogeneous groups of disorders with ineffective hematopoiesis and a varying propensity to evolve into acute myeloid leukemia (AML). Juvenile myelomonocytic leukemia (JMML) is a very aggressive myeloproliferative/ myelodysplastic disease characterized by excessive proliferation of granulocytic and monocytic cells and classified by the World Health Organization (WHO) as a separate entity between myeloproliferative neoplasms (MPN) and MDS. The diagnosis of JMML and MDS is difficult and based mainly on morphological criteria, although the revised WHO classification and the proposal of minimal diagnostic criteria have largely improved the diagnosis of these uncommon but appalling childhood diseases. The International Prognostic Scoring System for MDS, which is based on BM blast percentage, cytopenia and cytogenetics, showed limited value for the diagnosis of both MDS and JMML reflecting the differences between MDS and myeloproliferatives disease in children and adults. New molecular markers are being sought to make the diagnosis and the prognosis of these disorders more efficient and rapid. In this study we applied the most recent high throughput technologies (e.g. gene expression analysis, multiparametric phospho-flow cytometry and next generation sequencing) not only to provide new diagnostic and prognostic tools that can be used together with the well-established gold standard techniques but also to reveal new molecular pathways deregulated in these diseases. Using gene expression based classification we identified two different signatures in both MDS and JMML with clinical relevance. In MDS patients we identified a group with high risk of evolution into AML; similarly, in JMML patients the classification identified JMML with distinct prognosis outperforming all known clinical parameters in terms of prognostic relevance. Moreover, gene expression profiling in MDS patients provided new target genes involved in the leukemic process toward AML evolution. Aiming to provide a diagnostic tool for the rapid diagnosis of JMML, we used phospho-flow cytometry and created a simple algorithm for the diagnosis of JMML based on the percentage of p-STAT5 positive cells. Then, we assessed the feasibility and robustness of ultra deep sequencing technology to detect the mutation load in hematological malignancies. Finally, we report the case of two twins with concordant JMML providing new insight into the pathogenesis of the disease. In conclusion, all these data clearly showed that the application of gene expression profile analysis, next generation sequencing and phospho-flow cytometry for studying MDS and JMML provided new important tools for the prognosis and the diagnosis of these rare but aggressive diseases.
Le sindromi mielodisplastiche (MDS) e la leucemia mielomonocitica giovanile (JMML) sono dei rari disordini ematologici derivanti da disfunzioni a carico delle cellule staminali ematopoietiche e costituiscono meno del 5% di tutte le malattie neoplastiche ematologiche in età pediatrica. Le MDS sono un gruppo eterogeneo di malattie caratterizzate principalmente da un’ alterata maturazione delle cellule ematopoietiche della filiera mieloide e il maggiore fattore di rischio che le contraddistingue è l’evoluzione in leucemia mieloide acuta (LAM). La leucemia mielomonocitica giovanile è una malattia molto aggressiva, caratterizzata da un’ eccessiva proliferazione di cellule granulocitiche e monocitiche e viene classificata dall’ Organizzazione Mondiale della Sanità (OMS) come una entità separata con caratteristiche intermedie tra le neoplasie mieloproliferative e le sindromi mielodisplastiche. La diagnosi di MDS e JMML, basata principalmente su caratteristiche morfologiche, è molto difficile; tuttavia i nuovi criteri diagnostici recentemente introdotti nella classificazione OMS ne hanno facilitato la diagnosi. Attualmente la prognosi sull’ IPSS (International Prognostic Scoring System) dell’ adulto, un sistema di assegnazione dei pazienti a diverse fasce di rischio basato sulla percentuale di blasti nel midollo osseo, presenza o meno di citopenia e dati di citogenetica, mostra delle limitazioni quando applicato alle MDS e JMML pediatriche, riconducibile alle differenze esistenti tra le malattie mieloproliferative dell’adulto e del bambino. Nuovi markers biologici sono, perciò, necessari per migliorare non solo la diagnosi ma anche la prognosi di queste malattie. In questo studio abbiamo impiegato le più recenti tecnologie (i.e. l’analisi dell’ espressione genica, il sequenziamento di ultima generazione e la citofluorimetria per l’ identificazione dello stato di fosforilazione) non solo per fornire dei nuovi strumenti per la diagnosi e la prognosi da affiancare alle tecniche gold-standard ma anche per individuare nuovi processi biologici alterati in queste malattie. Mediante lo studio dell’espressione genica, abbiamo identificato la presenza di due sottogruppi con un diverso andamento clinico sia nelle MDS che nelle JMML. Abbiamo individuato i pazienti MDS con un più alto rischio di evoluzione in LAM e classificato i pazienti JMML in due gruppi con diversa prognosi; l’analisi dell’espressione genica nelle JMML è risultata essere uno strumento in grado di superare per valore prognostico tutti i criteri clinici attualmente impiegati. Inoltre, l’analisi del profilo d’ espressione genica nelle MDS ha permesso di individuare nuovi geni target coinvolti nella progressione verso la LAM. Al fine di fornire, poi, un nuovo strumento per la diagnosi di JMML abbiamo sviluppato un semplice algoritmo per la diagnosi di JMML basato sulla percentuale di cellule positive per STAT5 fosforilato in citofluorimetria. Abbiamo valutato, poi, la possibilità di applicare la tecnologia del sequenziamento di ultima generazione (ultra deep sequencing) per l’ identificazione della quantità di mutazioni presente nel midollo delle JMML. Infine, abbiamo riportato i dati relativi all’analisi di due gemelli concordanti con diagnosi di JMML al fine di fornire nuovi dati per la comprensione della patogenesi di questa malattia. In conclusione, abbiamo potuto dimostrare come l’impiego dello studio dell’espressione genica, del sequenziamento di ultima generazione e della citofluorimetria per l’identificazione di STAT5 fosforilato siano nuovi e validi strumenti per la prognosi e la diagnosi di queste rare malattie pediatriche.

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15

Purkayastha, Pratik. "Diagnostics and Prognostics of safety critical systems using machine learning, time and frequency domain analysis". Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-17603.

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The prime focus of this thesis was to develop a robust Prognostic and Diagnostic Health Management module (PDHM), capable of detecting faults, classifying faults, fault progression tracking and estimating time to failure. Priority was to obtain as much accuracy as possible with the bare minimum amount of sensors as possible. Algorithms like k-Nearest Neighbors (k-NN), Linear and Non- Linear regression and development of rule engine to identify safe operating limits were deployed. The entire solution was developed using R (v 3.5.0). The accuracy of around 98% was obtained in diagnostics. For Prognostics, our ability to predict time to failure more accurately increases with time. Some balance must be there between learning horizon and predicting horizon in order to get good predictions with reasonable time left to hit catastrophic failure. In conclusion, the PDHM module works just as desired and makes Predictive maintenance, smart replacement and crisis prediction possible ensuring the safety and security of people on board and assets.

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16

Löf, Liza. "Applications of in situ proximity ligation assays for cancer research and diagnostics". Doctoral thesis, Uppsala universitet, Molekylära verktyg, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-300191.

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In the field of cancer research and diagnostics it is crucial to have reliable methods for detecting molecules involved in the disease. New and better methods for diagnostics, prognostics and drug delivery therefore remain a permanent aim. In this thesis applications of the in situ proximity ligation assay (in situ PLA) were developed for diagnostics and research. Two new methods were developed, one more cost effective proximity assay without the use of enzymes and one method for loading pharmaceuticals in lipid rafts made from detergent resistant membranes (DRMs) to be used as a drug delivery platform. In Paper I the aim was to develop a flow cytometric detection method of the fusion protein BCR-ABL that is the hallmark of chronic myeloid leukemia (CML). By using in situ PLA the malignant cells carrying the fusion protein could be detected in patients in a convenient workflow. Paper II describes an application of multiplex in situ PLA, where extracellular vesicles (EVs) are detected and identified using flow cytometry. Up to five different antigens are targeted on the EVs, reflected in three different colors during detection in the flow cytometer. By using antibodies targeting proteins specific for prostasomes a population of prostasomes could be identified in human blood plasma. In Paper III a new method is described for using lipid raft for drug delivery. In this method, lipid rafts, derived from prostasomes or erythrocytes, are loaded with pharmaceuticals. The vehicles were loaded with doxorubicin, added to cells and counted. Cells that received the vehicle with doxorubicin stopped proliferating and died, while controls that received the lipid raft vehicle without doxorubicin were not affected, suggesting that the vehicles are effectively loaded with the drug and that they are safe. This lipid raft vehicle could provide a safe drug delivery system. Paper IV investigates the crosstalk between the two major signal pathways Hippo and Wnt, and how these are affected in gastric cancer. When looking at different colon cancer tumor stages, we found that the cellular localization of TAZ/β-catenin interactions were different. We also found that protein complexes involved in the crosstalk increased in sparsely growing cells compared to more densely growing cells. On the basis of these results the protein E-cadherin, involved in maintenance of the epithelial integrity, was investigated and was found to have a probable role in regulating the crosstalk between Hippo and Wnt. A new method for localized protein detection is described in paper V. Here a proximity assay, based on the hybridization chain reaction (HCR), was developed. This assay, proxHCR, is more cost effective than in situ PLA because no enzymes are required. ProxHCR successfully detects protein interactions and can be used together with both fluorescence microscopy and flow cytometry.

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17

Marinai, Luca. "Gas-path Diagnostics and Prognostics for Aero-engines Using Fuzzy Logic and Time Series Analysis". Thesis, Cranfield University, 2004. http://dspace.lib.cranfield.ac.uk/handle/1826/6730.

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Reducing the direct operating-costs is now crucial in order to ensure competitive advantages for airlines and manufacturers, and so effective advanced engine-condition monitoring methodologies are necessary. Hence gas-path diagnostics and prognostics methods are reviewed and the specifications for such effective tools deduced, together with their pertinent future prospects. First, the considerable value that a preliminary observability study adds to the diagnostics process was recognised. A secure procedure has been devised: it is capable of (i) the identification of the severity of correlations between any two of the available measurements, as well as the correlations between any two of the component changes, (ii) the identification of more complex correlations that involve more than two changes in performance parameters, and (iii) the quantification of the quality of the system observability through a pertinent parameter. This enables comparisons among a significant number of measurement set selections. The core of the research is a novel gas-path diagnostics (GPD) method that uses fuzzy logic in order to provide secure quantification of the gas-path component faults. A fuzzy diagnostics system was set up for the Rolls-Royce Trent 800 engine that relies on an extensive statement of fuzzy rules generated using an engine model to achieve a quantitative solution through a non-linear approach, which is competent to achieve (i) SFI (single fault isolation) in the presence of noisy data, (ii) tuning over a known global deterioration level for all the performance parameters (baseline) computed for the previous flight, (iii) partial MFI (multiple fault isolation) with up to 2 degraded components (i.e. 4 performance parameters) considerably faulty at the same time, (iv) SFI while isolating systematic errors in the measurements (biasses). A bias-tolerant system was devised by means of the NOT logical operator and a new formulation of the fuzzy rules that includes the location of the bias. An innovative prognostics framework was devised, which uses ARIMA models and regression models respectively for short and long term investigations, to compute forecasts and the associated prediction intervals, which are aimed at assisting the prognostics decision-making process. This is strictly related to the diverse business intentions: in this study safety and economic related applications are investigated. For example, the optimisation of the TBO (time between overhauls) considering maintenance cost and additional fuel cost due to the deterioration is studied and the potential cost savings for the operators highlighted. HMP 1.1 for performance analysis was developed: it is a health-monitoring andprognostics framework consisting of three modules that perform respectively observability study, gas-path diagnostics and prognostics. The substantial benefits that can be achieved with such a tool, relative to the enhanced maintenance planning and improved mission scheduling, are discussed in the thesis via applications to the Trent 800 engine.

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18

Gagar, Daniel Omatsola. "Validation and verification of the acoustic emission technique for structural health monitoring". Thesis, Cranfield University, 2013. http://dspace.lib.cranfield.ac.uk/handle/1826/8402.

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The performance of the Acoustic Emission (AE) technique was investigated to establish its reliability in detecting and locating fatigue crack damage as well as distinguishing between different AE sources in potential SHM applications. Experiments were conducted to monitor the AE signals generated during fatigue crack growth in coupon 2014 T6 aluminium. The influence of stress ratio, stress range, sample geometry and whether or not the load spectrum was of constant or variable amplitude were all investigated. Timing filters were incorporated to eliminate extraneous AE signals produced from sources other than the fatigue crack. AE signals detected were correlated with values of applied cyclic load throughout the tests. Measurements of Time difference of arrival were taken for assessment of errors in location estimates obtained using time of flight algorithms with a 1D location setup. It was found that there was significant variability in AE Hit rates in otherwise identical samples and test conditions. However common trends characteristic of all samples could be observed. At the onset of crack growth high AE Hit rates were observed for the first few millimetres after which they rapidly declined to minimal values for an extended period of crack growth. Another peak and then decline in AE Hit rates was observed for subsequent crack growth before yet another increase as the sample approached final failure. The changes in AE signals with applied cyclic load provided great insights into the different AE processes occurring during crack growth. AE signals were seen to occur in the lower two-thirds of the maximum load in the first few millimetres of crack growth before occurring at progressively smaller values as the crack length increased. These emissions could be associated with crack closure. A separate set of AE signals were observed close to the maximum cyclic stress throughout the entire crack growth process. At the failure crack length AE signals were generated across the entire loading range. Novel metrics were developed to statistically characterise variability of AE generation with crack growth and at particular crack lengths across different samples. A novel approach for fatigue crack length estimation was developed based on monitoring applied loads to the sample corresponding with generated AE signals which extends the functionality of the AE technique in an area which was previously deficient. It is however limited by its sensitivity to changes in sample geometry. Experiments were also performed to validate the performance of the AE technique in detecting and locating fatigue crack in a representative wing-box structure. An acousto-ultrasonic method was used to calibrate the AE wave velocity in the structure which was used to successfully locate the 'hidden' fatigue crack. A novel observation was made in the series of tests conducted where the complex propagation paths in the structure could be exploited to perform wide area sensing coverage in certain regions using sensors mounted on different components of the structure. This also extends current knowledge on the capability of the AE technique.

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19

Taylor, G. Scott. "Design and Development of Oligonucleotide Microarrays and their Application in Diagnostic and Prognostic Estimation of Human Gliomas". VCU Scholars Compass, 2006. http://scholarscompass.vcu.edu/etd/1459.

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DNA microarrays represent an ultra-high throughput gene expression assay employed to study the transcriptomic profiles of biological tissues. These devices are increasingly being used to study many aspects of gene regulation, and there is growing interest in the biotechnology and pharmaceutical industries for developing such devices in efforts toward rational product/drug design. The DNA microarray also provides a unique and objective means for diagnosis and prognosis of human diseases based on patterns of gene expression. This is especially important in cancer research and the thrust toward personalized medicine. This dissertation details the design and development of oligonucleotide microarrays and the design and execution of a gene expression study conducted using human glioma specimines. Chapter 2 details the design and development a ~10,000 gene human oligonucleotide microarray. This device consisted of a 21,168 features, each composed of a particular human gene-probe and was applied to the challenge of diagnostic and prognostic estimation for human gliomas (chapter 3). Gliomas are the most frequent and deadly neoplasms of the human brain characterized by a high misdiagnosis rate and low survival. The study in chapter 3 demonstrated that the specified design and development parameters were appropriate for conducting gene expression analysis and that this platform can be used successfully to predict malignancy grade and survival for glioma patients.

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20

Alrabady, Linda Antoun Yousef. "An online-integrated condition monitoring and prognostics framework for rotating equipment". Thesis, Cranfield University, 2014. http://dspace.lib.cranfield.ac.uk/handle/1826/9204.

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Detecting abnormal operating conditions, which will lead to faults developing later, has important economic implications for industries trying to meet their performance and production goals. It is unacceptable to wait for failures that have potential safety, environmental and financial consequences. Moving from a “reactive” strategy to a “proactive” strategy can improve critical equipment reliability and availability while constraining maintenance costs, reducing production deferrals, decreasing the need for spare parts. Once the fault initiates, predicting its progression and deterioration can enable timely interventions without risk to personnel safety or to equipment integrity. This work presents an online-integrated condition monitoring and prognostics framework that addresses the above issues holistically. The proposed framework aligns fully with ISO 17359:2011 and derives from the I-P and P-F curve. Depending upon the running state of machine with respect to its I-P and P-F curve an algorithm will do one of the following: (1) Predict the ideal behaviour and any departure from the normal operating envelope using a combination of Evolving Clustering Method (ECM), a normalised fuzzy weighted distance and tracking signal method. (2) Identify the cause of the departure through an automated diagnostics system using a modified version of ECM for classification. (3) Predict the short-term progression of fault using a modified version of the Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS), called here MDENFIS and a tracking signal method. (4) Predict the long term progression of fault (Prognostics) using a combination of Autoregressive Integrated Moving Average (ARIMA)- Empirical Mode Decomposition (EMD) for predicting the future input values and MDENFIS for predicting the long term progression of fault (output). The proposed model was tested and compared against other models in the literature using benchmarks and field data. This work demonstrates four noticeable improvements over previous methods: (1) Enhanced testing prediction accuracy, (2) comparable processing time if not better, (3) the ability to detect sudden changes in the process and finally (4) the ability to identify and isolate the problem source with high accuracy.

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21

Zhang, Guangfan. "Optimum Sensor Localization/Selection In A Diagnostic/Prognostic Architecture". Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/6846.

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Optimum Sensor Localization/Selection in A Diagnostic/Prognostic Architecture Guangfan Zhang 107 Pages Directed by Dr. George J. Vachtsevanos This research addresses the problem of sensor localization/selection for fault diagnostic purposes in Prognostics and Health Management (PHM)/Condition-Based Maintenance (CBM) systems. The performance of PHM/CBM systems relies not only on the diagnostic/prognostic algorithms used, but also on the types, location, and number of sensors selected. Most of the research reported in the area of sensor localization/selection for fault diagnosis focuses on qualitative analysis and lacks a uniform figure of merit. Moreover, sensor localization/selection is mainly studied as an open-loop problem without considering the performance feedback from the on-line diagnostic/prognostic system. In this research, a novel approach for sensor localization/selection is proposed in an integrated diagnostic/prognostic architecture to achieve maximum diagnostic performance. First, a fault detectability metric is defined quantitatively. A novel graph-based approach, the Quantified-Directed Model, is called upon to model fault propagation in complex systems and an appropriate figure-of-merit is defined to maximize fault detectability and minimize the required number of sensors while achieving optimum performance. Secondly, the proposed sensor localization/selection strategy is integrated into a diagnostic/prognostic system architecture while exhibiting attributes of flexibility and scalability. Moreover, the performance is validated and verified in the integrated diagnostic/prognostic architecture, and the performance of the integrated diagnostic/prognostic architecture acts as useful feedback for further optimizing the sensors considered. The approach is tested and validated through a five-tank simulation system. This research has led to the following major contributions: ??generalized methodology for sensor localization/selection for fault diagnostic purposes. ??quantitative definition of fault detection ability of a sensor, a novel Quantified-Directed Model (QDG) method for fault propagation modeling purposes, and a generalized figure of merit to maximize fault detectability and minimize the required number of sensors while achieving optimum diagnostic performance at the system level. ??novel, integrated architecture for a diagnostic/prognostic system. ??lidation of the proposed sensor localization/selection approach in the integrated diagnostic/prognostic architecture.

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22

Shao, Huilin. "Biosensor Platforms for Molecular Analyses of Circulating Cancer Biomarkers". Thesis, Harvard University, 2013. http://dissertations.umi.com/gsas.harvard:11134.

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Solid cancers often shed (sub)cellular materials into the circulation, such as circulating tumor cells and extracellular microvesicles. Mounting evidence supports that these circulating materials could serve as surrogate cancer markers for classifying primary tumors, stratifying patients for targeted therapies, assessing treatment efficacy, and achieving clinical benefits. A sensor platform capable of sensitive and portable detection of circulating cancer markers would thus be an invaluable tool, that will advance our understanding of tumor biology as well as clinical outcomes. This dissertation describes various systems that we have developed for quantitative analyses of circulating cancer biomarkers. Firstly, we have developed a novel magnetic resonance sensing platform for microvesicle analyses. By using a chip-based platform that combines microfiltration and bioorthogonal nanoparticle targeting, we demonstrate for the first time that magnetic biosensing can be applied for clinical evaluation of circulating microvesicles in blood samples to monitor cancer therapy. Secondly, we have advanced a new plasmonic sensor to achieve label-free detection of microvesicles. Based on periodic nanohole arrays, this platform has been applied for high-throughput protein profiling of microvesicles in native ascites. Finally, we have implemented microfluidic devices to effectively enrich circulating tumor cells from peripheral whole blood, and to enable comprehensive molecular analyses of isolated tumor cells at a single cell resolution. By enabling rapid, sensitive and cost-effective detection of circulating cancer markers, these developed platforms could significantly expand the reach of preclinical and clinical cancer research, in informing therapy selection, rationally directing trials, and improving sequential monitoring to achieve better clinical outcomes.

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23

Ellis, Brian. "A real-time hybrid method based on blade tip timing for diagnostics and prognostics of cracks in turbomachine rotor blades". Diss., University of Pretoria, 2019. http://hdl.handle.net/2263/73315.

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This dissertation proposes hybrid models for (i) diagnosis and (ii) remaining useful life estimation of a single fatigue crack in a low-pressure turbine blade. The proposed hybrid methods consist of physics-based methods and data-driven methods. In this dissertation, blade tip timing is used to measure the relative tip displacement of a rotor blade. The natural frequency of the blade is determined by detecting the critical speeds of the blade using a newly derived least squares spectral analysis method. The method shares its origin from the Lomb-Scargle periodogram and can detect resonance frequencies in the blade’s displacement while the rotor is in operation. A Campbell diagram is then used to convert the critical speed into a natural frequency. Two kinds of shaft transients are considered, a run-up run-down crossing the same critical speed, is used to test the new method. This dissertation shows that the relative displacement of the blade tip is comparable to those simulated from an analytical single degree of freedom model. It is also shown that the newly proposed resonance detection method estimates the natural frequency of the blade to a high degree of accuracy when compared to the measurements from a modal impact hammer test. The natural frequency obtained from the real time measurement is then used in a pre-constructed hybrid diagnostics model. The diagnostics model provides a probability density function estimation of the surface crack length given the measured natural frequency. A Gaussian Process Regression model is trained on data collected during experiments and finite element simulations of a fatigue crack in the blade. The final part of this dissertation is a sequential inference model for improving the estimation of the crack length and the prediction of the crack growth. The suggested model uses an unscented Kalman filter that improves estimations of the crack length and the rate of crack growth from Paris’ Law coefficients. The model is updated each time a diagnosis is performed on the blade. The RUL of the blade is then determined from an integration of Paris’s Law given the uncertainty estimates of the current damage in the blade. The result of the algorithm is an estimation of the remaining number of cycles to failure. The algorithm is shown to improve the overall estimation of the RUL; however, it is suggested that future work looks at the convergence rate of the method.
Dissertation (MEng)--University of Pretoria, 2019.
Eskom Power Plant Engineering Institute (EPPEI)
Mechanical and Aeronautical Engineering
MEng
Unrestricted

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24

Diallo, Ousmane Nasr. "A data analytics approach to gas turbine prognostics and health management". Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/42845.

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As a consequence of the recent deregulation in the electrical power production industry, there has been a shift in the traditional ownership of power plants and the way they are operated. To hedge their business risks, the many new private entrepreneurs enter into long-term service agreement (LTSA) with third parties for their operation and maintenance activities. As the major LTSA providers, original equipment manufacturers have invested huge amounts of money to develop preventive maintenance strategies to minimize the occurrence of costly unplanned outages resulting from failures of the equipments covered under LTSA contracts. As a matter of fact, a recent study by the Electric Power Research Institute estimates the cost benefit of preventing a failure of a General Electric 7FA or 9FA technology compressor at $10 to $20 million. Therefore, in this dissertation, a two-phase data analytics approach is proposed to use the existing monitoring gas path and vibration sensors data to first develop a proactive strategy that systematically detects and validates catastrophic failure precursors so as to avoid the failure; and secondly to estimate the residual time to failure of the unhealthy items. For the first part of this work, the time-frequency technique of the wavelet packet transforms is used to de-noise the noisy sensor data. Next, the time-series signal of each sensor is decomposed to perform a multi-resolution analysis to extract its features. After that, the probabilistic principal component analysis is applied as a data fusion technique to reduce the number of the potentially correlated multi-sensors measurement into a few uncorrelated principal components. The last step of the failure precursor detection methodology, the anomaly detection decision, is in itself a multi-stage process. The obtained principal components from the data fusion step are first combined into a one-dimensional reconstructed signal representing the overall health assessment of the monitored systems. Then, two damage indicators of the reconstructed signal are defined and monitored for defect using a statistical process control approach. Finally, the Bayesian evaluation method for hypothesis testing is applied to a computed threshold to test for deviations from the healthy band. To model the residual time to failure, the anomaly severity index and the anomaly duration index are defined as defects characteristics. Two modeling techniques are investigated for the prognostication of the survival time after an anomaly is detected: the deterministic regression approach, and parametric approximation of the non-parametric Kaplan-Meier plot estimator. It is established that the deterministic regression provides poor prediction estimation. The non parametric survival data analysis technique of the Kaplan-Meier estimator provides the empirical survivor function of the data set comprised of both non-censored and right censored data. Though powerful because no a-priori predefined lifetime distribution is made, the Kaplan-Meier result lacks the flexibility to be transplanted to other units of a given fleet. The parametric analysis of survival data is performed with two popular failure analysis distributions: the exponential distribution and the Weibull distribution. The conclusion from the parametric analysis of the Kaplan-Meier plot is that the larger the data set, the more accurate is the prognostication ability of the residual time to failure model.

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25

Galloway, Grant Stewart. "Developing anomaly detection, diagnostics and prognostics for condition monitoring with limited historical data in new applications such as tidal power". Thesis, University of Strathclyde, 2017. http://digitool.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=28669.

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Tidal power is a promising source of renewable energy worldwide, more stable and predictable than alternatives such as offshore wind power. However, the harsh operating environment makes maintenance of tidal turbines difficult and costly to perform. Intelligent condition monitoring systems, utilised as part of a condition-based maintenance strategy, can provide operators with timely and accurate indications of faults before serious damage occurs. Nevertheless, tidal technology is a new application, where deployments are currently in its infancy. Therefore, there is limited practical experience of how faults will develop within tidal turbines in operation. This thesis aims to investigate the requirements of condition monitoring methods, from both theory and knowledge of similar fields (such as offshore wind), to develop an approach for applying condition monitoring in new applications. This work first investigates the response of a commercial-scale tidal turbine in operation through a data mining analysis of condition data from the Andritz Hydro Hammerfest HS1000 tidal turbine. Data mining was performed following the CRISP-DM methodology and was used to build models of the normal condition response of turbine components, from which anomalous behaviour indicative of the development of faults can be detected. This approach was then expanded to include both diagnostic and prognostic modelling, where faults can be automatically classified and the remaining useful life of equipment undergoing degradation can be estimated. This has resulted in a generalised framework, based on CRISP-DM, that can be applied to perform condition monitoring in new applications, learning from the response of machinery over time during its operation.

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26

Friedrich, David [Verfasser], Dietrich [Akademischer Betreuer] Meyer-Ebrecht, Bastian [Akademischer Betreuer] Leibe y Alfred [Akademischer Betreuer] Böcking. "Effective improvement of cancer diagnostics and prognostics by computer-assisted cell image analysis / David Friedrich ; Dietrich Meyer-Ebrecht, Bastian Leibe, Alfred Böcking". Aachen : Universitätsbibliothek der RWTH Aachen, 2016. http://d-nb.info/1129260925/34.

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27

Lamanda, Ariana Corinne. "Alternating Current Electrokinetic Manipulation and Concentration of Free Circulating DNA from Blood Samples". Thesis, The University of Arizona, 2014. http://hdl.handle.net/10150/332828.

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Molecular analysis of free circulating (fc)DNA has the potential to change the face of medicine, specifically in cancer diagnostics and in monitoring the efficacy of cancer treatments. In this study, a microfluidic device using AC electrokinetics is developed for rapid concentration and detection of fcDNA from blood. The device concentrates fcDNA using a combination of AC electrothermal flow and dielectrophoresis. The electrothermal fluid motion drives fcDNA towards the center of the electrode where dielectrophoretic trapping occurs. Once fcDNA is collected at the center, the concentration in the sample can be determined by fluorescent analysis using an intercalating dye binding to the double-stranded DNA. Effects of operating parameters are investigated to optimize the device's design. The electrokinetic device isolates high molecular weight DNA and can distinguish from low molecular weight DNA. Quantitative detection of fcDNA in physiologically relevant concentrations is demonstrated toward rapid diagnostics of cancer and monitoring of treatment efficacy.

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28

Abbas, Manzar. "System-level health assessment of complex engineered processes". Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37260.

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Condition-Based Maintenance (CBM) and Prognostics and Health Management (PHM) technologies aim at improving the availability, reliability, maintainability, and safety of systems through the development of fault diagnostic and failure prognostic algorithms. In complex engineering systems, such as aircraft, power plants, etc., the prognostic activities have been limited to the component-level, primarily due to the complexity of large-scale engineering systems. However, the output of these prognostic algorithms can be practically useful for the system managers, operators, or maintenance personnel, only if it helps them in making decisions, which are based on system-level parameters. Therefore, there is an emerging need to build health assessment methodologies at the system-level. This research employs techniques from the field of design-of-experiments to build response surface metamodels at the system-level that are built on the foundations provided by component-level damage models.

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29

Losik, Len. "Using Data-Driven Prognostic Algorithms for Completing Independent Failure Analysis". International Foundation for Telemetering, 2011. http://hdl.handle.net/10150/595768.

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ITC/USA 2011 Conference Proceedings / The Forty-Seventh Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2011 / Bally's Las Vegas, Las Vegas, Nevada
Current failure analysis practices use diagnostic technology developed over the past 100 years of designing and manufacturing electrical and mechanical equipment to identify root cause of equipment failure requiring expertise with the equipment under analysis. If the equipment that failed had telemetry embedded, prognostic algorithms can be used to identify the deterministic behavior in completely normal appearing data from fully functional equipment used for identifying which equipment will fail within 1 year of use, can also identify when the presence of deterministic behavior was initiated for any equipment failure.

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30

Losik, Len. "Using Generic Telemetry Prognostic Algorithms for Launch Vehicle and Spacecraft Independent Failure Analysis Service". International Foundation for Telemetering, 2010. http://hdl.handle.net/10150/605927.

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ITC/USA 2010 Conference Proceedings / The Forty-Sixth Annual International Telemetering Conference and Technical Exhibition / October 25-28, 2010 / Town and Country Resort & Convention Center, San Diego, California
Current failure analysis practices use diagnostic technology developed over the past 100 years of designing and manufacturing electrical and mechanical equipment to identify root cause of equipment failure requiring expertise with the equipment under analysis. If the equipment that failed had telemetry embedded, prognostic algorithms can be used to identify the deterministic behavior in completely normal appearing data from fully functional equipment used for identifying which equipment will fail within 1 year of use, can also identify when the presence of deterministic behavior was initiated for any equipment failure.

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31

Losik, Len. "Using Generic Telemetry Prognostic Algorithms for Launch Vehicle and Spacecraft Independent Failure Analysis Service". International Foundation for Telemetering, 2009. http://hdl.handle.net/10150/606037.

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ITC/USA 2009 Conference Proceedings / The Forty-Fifth Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2009 / Riviera Hotel & Convention Center, Las Vegas, Nevada
Current equipment and vehicle failure analysis practices use diagnostic technology developed over the past 100 years of designing and manufacturing electrical and mechanical equipment to identify root cause of equipment failure requiring expertise with the equipment under analysis. If the equipment that failed had telemetry embedded, prognostic algorithms can be used to identify the deterministic behavior in completely normal appearing data from fully functional equipment used for identifying which equipment will fail within 1 year of use, can also identify when the presence of deterministic behavior was initiated for any equipment failure.

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32

Geanta, Ioana. "Contribution à un cadre de modélisation de gestion intégrée de l'état de santé de véhicules : proposition d'un module générique de gestion de la santé suport à l'intégration du diagnostic et du pronostic". Thesis, Université de Lorraine, 2014. http://www.theses.fr/2014LORR0212/document.

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Spherea (anciennement Cassidian Test & Services), initiateur de la thèse, est un des leaders sur le marché des systèmes automatiques de test (ATE) pour la maintenance des véhicules aéronautiques et de défense. L’intérêt de la société dans la recherche en gestion intégrée de la santé de véhicules est motivé par le taux élevé de fausses déposes d’équipements survenues lors de la maintenance opérationnelle, détectées par les ATE. Ceci engendre des actions de maintenance superflues, et par conséquent des pertes majeurs de temps, d'énergie et d'argent. L’IVHM, de par ses capacités avancées de diagnostic et de pronostic, et son intégration au niveau de l'entreprise de la gestion de santé de véhicules pourrait permettre la réduction des taux de NFF. Néanmoins, les solutions de systèmes IVHM proposées par les communautés scientifique et industrielle sont la plupart du temps développées pour des systèmes spécifiques, et souvent fondées sur des concepts propriétaires. Cela a pour conséquence un manque de consensus à la fois dans les principes structurants des systèmes IVHM et dans leur ingénierie. Aujourd'hui, un défi majeur est de fournir un cadre de modélisation d’IVHM indépendamment du type de système d’intérêt, soutenant l’ingénierie des systèmes IVHM. Vers ce cadre, les principales contributions développées dans cette thèse construisent progressivement les fondations et les piliers d'un cadre de modélisation d’IVHM. La proposition, dans une vision système, des principes structurants d’un système de systèmes permet de définir au général un système IVHM. A partir de cette vision système, le focus de la thèse est orienté sur la gestion de santé du véhicule, fonction de l’IVHM centrée sur le véhicule constituant le catalyseur des décisions de maintenance au niveau opérationnel, ayant la capacité de résoudre le problème industriel à la genèse de la thèse. Les principes structurant clés de cette fonction en trois dimensions (dimension fonctionnelles, dimension d’abstraction, dimension de distribution entre le segment embarqué/sol) sont à la base de la proposition d’un cadre générique de modélisation d’IVHM considérant à la fois les fonctions internes et externes au véhicule. Ce cadre est investigué en cohérence avec une approche construite sur les modèles (MBSE). Conformément à cette approche MBSE, la modélisation, au sein de ce cadre d’IVHM, du module générique de gestion de la santé (gHMM) constitue le support d’intégration des fonctionnalités de diagnostic et de pronostic. Cette modélisation repose sur une vision « boîte noire » et « boîte blanche » du module traduite par un ensemble cohérent de diagrammes SysML, et se basant sur les structures de données standardisées d’OSA-CBM. La formalisation du gHMM permet d'intégrer le diagnostic et le pronostic, processus clés de l’IVHM, dans son sens conventionnel : du diagnostic vers le pronostic, que dans un sens original : du pronostic vers le diagnostic. Ce dernier sens est illustré par la proposition d'un algorithme support à une activité élémentaire du gHMM dans la finalité de réduire les groupes d’ambiguïtés dans le diagnostic. Cette ingénierie aboutit par conséquent à un cadre générique de modélisation, qui par un principe d’instanciation, permet la construction d’une architecture de gestion de l’état de santé d’un système IVHM particulier. Vers cette instanciation la thèse examine les caractéristiques qui impactent la conception d’architectures de gestion de la santé et la sélection d’algorithmes supportant ces architectures, et en propose une formalisation basée sur les ontologies pour la sélection multicritères d’algorithmes de diagnostic et de pronostic appropriés pour la gestion de la santé de véhicules. Finalement, le protocole de validation de l’ensemble des contributions est proposé et illustré à des échelles différentes pour la gestion de l’état de santé d’éoliennes et de drones
Spherea (formerly Cassidian Test & Services), initiator of the PhD thesis, is a leading provider of Automatic Test Equipment (ATE) solutions for aerospace and military vehicles’ maintenance. The company’s interest in Integrated Vehicle Health Management (IVHM) research is motivated by occurrence of No Fault Found (NFF) events detected by ATE, and determining superfluous maintenance activities and consequently major wastes of time, energy and money. IVHM, through its advanced diagnostics and prognostics capabilities, and integration at enterprise level of vehicle health management could solve NFF events occurring during operational-level maintenance. Nevertheless, IVHM systems proposed so far are most of the times developed and matured empirically, for specific vehicle systems, founded on proprietary concepts, and lacking of consensual structuring principles. This results in a lack of consensus in both the structuring principles of IVHM systems and their Systems Engineering. Today, the challenge is to provide an IVHM modelling framework independent from the type of supported system and usable for IVHM Systems Engineering. Towards such framework, the main contributions developed in this thesis progressively build the foundation and pillars of an IVHM modelling framework. The notion of system of systems drives our first proposal of defining principles of an overall IVHM system. From this system vision, the focus of the thesis is oriented on the vehicle centric function of IVHM as catalyst of maintenance decisions at operational level, having the ability to solve the industrial problems at the genesis of the thesis. The key structuring principles of this function are analysed upon three dimensions (functional dimension, a dimension of abstraction, and distribution between the on-board /on-ground segment), setting the basis of the proposal of a generic modelling framework IVHM, considering both vehicle and enterprise centric functions. This framework is built following a Model-based Systems Engineering (MBSE) approach, supported by SysML. The major contribution of the thesis is the modelling, within the framework of IVHM, of the generic Health Management Module (gHMM), support for integration of diagnostics and prognostics, key processes of health management. The gHMM formalization enables to integrate diagnostics and prognostics not only in the conventional way: from diagnosis to prognosis, but also in an original one: from prognostics to diagnostics with the purpose of reducing ambiguity groups; the latter is backed-up through the proposal of an algorithm for one elementary activities of the gHMM. The gHMM MBSE engineering thus leads to a generic modelling framework, which, by a principle of instantiation, allows the construction of an IVHM system designed for the health management of individual vehicle systems. Towards such particularization, the thesis investigates characteristics impacting selection of appropriate supporting algorithms. This analysis enables to identify ten generic macro-criteria, which are further formalized based on ontologies and used within a multi-criteria based methodology suited for selecting diagnostics and prognostics algorithms for vehicle health management. Finally, the validation protocol of the scientific contributions is proposed, and applied at different scales of implementation in the field of wind turbine and UAV health management

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33

Isaksson, Olle. "Model-based Diagnosis of a Satellite Electrical Power System with RODON". Thesis, Linköping University, Linköping University, Vehicular Systems, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-16763.

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As space exploration vehicles travel deeper into space, their distance to earth increases.The increased communication delays and ground personnel costs motivatea migration of the vehicle health management into space. A way to achieve thisis to use a diagnosis system. A diagnosis system uses sensor readings to automaticallydetect faults and possibly locate the cause of it. The diagnosis system usedin this thesis is a model-based reasoning tool called RODON developed by UptimeSolutions AB. RODON uses information of both nominal and faulty behavior ofthe target system mathematically formulated in a model.The advanced diagnostics and prognostics testbed (ADAPT) developed at theNASA Ames Research Center provides a stepping stone between pure researchand deployment of diagnosis and prognosis systems in aerospace systems. Thehardware of the testbed is an electrical power system (EPS) that represents theEPS of a space exploration vehicle. ADAPT consists of a controlled and monitoredenvironment where faults can be injected into a system in a controlled manner andthe performance of the diagnosis system carefully monitored. The main goal of thethesis project was to build a model of the ADAPT EPS that was used to diagnosethe testbed and to generate decision trees (or trouble-shooting trees).The results from the diagnostic analysis were good and all injected faults thataffected the actual function of the EPS were detected. All sensor faults weredetected except faults in temperature sensors. A less detailed model would haveisolated the correct faulty component(s) in the experiments. However, the goal wasto create a detailed model that can detect more than the faults currently injectedinto ADAPT. The created model is stationary but a dynamic model would havebeen able to detect faults in temperature sensors.Based on the presented results, RODON is very well suited for stationary analysisof large systems with a mixture of continuous and discrete signals. It is possibleto get very good results using RODON but in turn it requires an equally goodmodel. A full analysis of the dynamic capabilities of RODON was never conductedin the thesis which is why no conclusions can be drawn for that case.

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34

Kan, Man Shan. "Multi-sensor condition monitoring of bearings using support vector machines". Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/110621/1/Man%20Shan_Kan_Thesis.pdf.

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This thesis presents a study on bearing condition monitoring under variable operating conditions using Support Vector Machines. Data collected from multiple sensors including accelerometers, acoustic emission sensors and tachometers are used for the studies presented in this thesis. This work has successfully demonstrated acoustic emission's superiority in bearing incipient fault detection; and the prognostic study has developed an effective prognostic approach to capture the system's dynamics with speed variations and make accurate predictions.

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35

Jose, Sagar. "Stratégies d'apprentissage multimodal pour le diagnostic et le pronostic de la santé des machines industrielles dans un contexte de manque de données". Electronic Thesis or Diss., Université de Toulouse (2023-....), 2024. http://www.theses.fr/2024TLSEP093.

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Les approches de Pronostic et gestion de la santé des systèmes (Prognostics and Health Management : PHM) guidées par les données sont fortement dépendantes de la disponibilité et de la qualité d’historiques de défaillances, une exigence souvent difficile à satisfaire dans les systèmes de surveillance en conditions industrielles. Cette divergence crée un écart significatif entre les méthodologies de PHM et leur application pratique sur des systèmes réels. L’accent prédominant mis sur les ensembles de données unimodales dans les travaux de recherche en PHM basée sur les données met en lumière le potentiel des données multimodales pour combler cet écart.Cette thèse explore l’intégration des données multimodales afin d’améliorer les méthodes et les algorithmes de PHM appliqués aux machines industrielles. Elle aborde de manière systématique des défis cruciaux tels que l’absence de données, les données bruitées, les données clairsemées et irrégulières, le déséquilibre des classes et la rareté des données de fonctionnement jusqu’à la défaillance. Elle propose des méthodologies innovantes qui intègrent plusieurs modalités de données et exploitent l’expertise spécifique au domaine pour créer des modèles prédictifs robustes.Les contributions principales de la thèse se déclinent comme suit :1. Apprentissage basé sur l’attention intermodale : une nouvelle méthode d’apprentissage multimodal conçue pour atténuer les limites posées par les données manquantes et bruitées. Elle permet d’intégrer des informations provenant de multiples modalités, améliorant ainsi la précision et la robustesse des modèles prédictifs.2. Méthodologie de diagnostic multimodal assisté par les connaissances d’experts : cette méthodologie combine l’expertise du domaine avec l’apprentissage multimodal pour permettre des diagnostics complets, améliorant ainsi la détection et la classification des défauts dans les machines industrielles.3. Approche de pronostic basée sur des graphes : cette approche innovante construit des trajectoires de fonctionnement jusqu’à la défaillance à partir de données incomplètes en utilisant des techniques basées sur les graphes, offrant une avancée significative dans le domaine du pronostic de défaillances.Ces méthodologies ont été rigoureusement validées sur des données de simulation ainsi que sur des données industrielles provenant d’hydro-générateurs, démontrant des améliorations significatives des algorithmes de PHM et de maintenance prédictive. Les résultats soulignent le potentiel des données multimodales pour améliorer considérablement la fiabilité et l’efficacité des modèles de PHM.Cette thèse apporte un cadre complet pour exploiter diverses sources de données et l’expertise du domaine, promettant de transformer les stratégies de maintenance et de réduire les coûts opérationnels dans diverses industries. Les résultats ouvrent la voie à des recherches futures et à des applications pratiques, positionnant l’intégration des données multimodales comme une avancée essentielle dans le domaine du PHM
Prognostics and Health Management (PHM) with data-driven techniques is heavily dependent upon the availability of extensive and high-quality datasets, a requirement often challenging to fulfill in industrial condition monitoring environments. This discrepancy creates a significant gap between state-of-the-art PHM methodologies and their practical application in real-world scenarios. The prevailing focus in data-driven PHM research on unimodal datasets highlights the potential of multimodal data to bridge this gap.This thesis explores the integration of multimodal data to advance PHM models for industrial machines. It systematically addresses pivotal challenges such as data missingness and noise, sparse and irregular datasets, class imbalance, and the scarcity of run-to-failure data. The research develops innovative methodologies that incorporate multiple data modalities and harness domain-specific expertise to create robust predictive models.The primary contributions of this research include:1. Cross-modal attention-based learning: A new multimodal learning method is designed to mitigate the limitations posed by missing and noisy data. It allows integrating information across multiple modalities, thereby enhancing the accuracy and robustness of predictive models.2. Expert-knowledge-assisted multimodal diagnostics methodology: This methodology combines domain expertise with multimodal learning to enable comprehensive diagnostics, thereby improving fault detection and classification in industrial machinery.3. Graph-based prognostics approach: This innovative approach constructs run-to-failure trajectories from incomplete data using graph-based techniques, offering a significant advancement in failure prognostics.These methodologies were rigorously validated using both simulation and industrial dataset of hydrogenerators, demonstrating significant improvements in PHM and predictive maintenance capabilities. The results underscore the potential of multimodal data to significantly enhance the reliability and efficiency of PHM methods and algorithms.This thesis proposes a comprehensive framework for leveraging diverse data sources and domain expertise, promising to transform maintenance strategies and reducing operational costs across various industries. The findings pave the way for future research and practical implementations, positioning multimodal data integration as a pivotal advancement in the field of PHM

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36

Geanta, Ioana. "Contribution à un cadre de modélisation de gestion intégrée de l'état de santé de véhicules : proposition d'un module générique de gestion de la santé suport à l'intégration du diagnostic et du pronostic". Electronic Thesis or Diss., Université de Lorraine, 2014. http://www.theses.fr/2014LORR0212.

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Spherea (anciennement Cassidian Test & Services), initiateur de la thèse, est un des leaders sur le marché des systèmes automatiques de test (ATE) pour la maintenance des véhicules aéronautiques et de défense. L’intérêt de la société dans la recherche en gestion intégrée de la santé de véhicules est motivé par le taux élevé de fausses déposes d’équipements survenues lors de la maintenance opérationnelle, détectées par les ATE. Ceci engendre des actions de maintenance superflues, et par conséquent des pertes majeurs de temps, d'énergie et d'argent. L’IVHM, de par ses capacités avancées de diagnostic et de pronostic, et son intégration au niveau de l'entreprise de la gestion de santé de véhicules pourrait permettre la réduction des taux de NFF. Néanmoins, les solutions de systèmes IVHM proposées par les communautés scientifique et industrielle sont la plupart du temps développées pour des systèmes spécifiques, et souvent fondées sur des concepts propriétaires. Cela a pour conséquence un manque de consensus à la fois dans les principes structurants des systèmes IVHM et dans leur ingénierie. Aujourd'hui, un défi majeur est de fournir un cadre de modélisation d’IVHM indépendamment du type de système d’intérêt, soutenant l’ingénierie des systèmes IVHM. Vers ce cadre, les principales contributions développées dans cette thèse construisent progressivement les fondations et les piliers d'un cadre de modélisation d’IVHM. La proposition, dans une vision système, des principes structurants d’un système de systèmes permet de définir au général un système IVHM. A partir de cette vision système, le focus de la thèse est orienté sur la gestion de santé du véhicule, fonction de l’IVHM centrée sur le véhicule constituant le catalyseur des décisions de maintenance au niveau opérationnel, ayant la capacité de résoudre le problème industriel à la genèse de la thèse. Les principes structurant clés de cette fonction en trois dimensions (dimension fonctionnelles, dimension d’abstraction, dimension de distribution entre le segment embarqué/sol) sont à la base de la proposition d’un cadre générique de modélisation d’IVHM considérant à la fois les fonctions internes et externes au véhicule. Ce cadre est investigué en cohérence avec une approche construite sur les modèles (MBSE). Conformément à cette approche MBSE, la modélisation, au sein de ce cadre d’IVHM, du module générique de gestion de la santé (gHMM) constitue le support d’intégration des fonctionnalités de diagnostic et de pronostic. Cette modélisation repose sur une vision « boîte noire » et « boîte blanche » du module traduite par un ensemble cohérent de diagrammes SysML, et se basant sur les structures de données standardisées d’OSA-CBM. La formalisation du gHMM permet d'intégrer le diagnostic et le pronostic, processus clés de l’IVHM, dans son sens conventionnel : du diagnostic vers le pronostic, que dans un sens original : du pronostic vers le diagnostic. Ce dernier sens est illustré par la proposition d'un algorithme support à une activité élémentaire du gHMM dans la finalité de réduire les groupes d’ambiguïtés dans le diagnostic. Cette ingénierie aboutit par conséquent à un cadre générique de modélisation, qui par un principe d’instanciation, permet la construction d’une architecture de gestion de l’état de santé d’un système IVHM particulier. Vers cette instanciation la thèse examine les caractéristiques qui impactent la conception d’architectures de gestion de la santé et la sélection d’algorithmes supportant ces architectures, et en propose une formalisation basée sur les ontologies pour la sélection multicritères d’algorithmes de diagnostic et de pronostic appropriés pour la gestion de la santé de véhicules. Finalement, le protocole de validation de l’ensemble des contributions est proposé et illustré à des échelles différentes pour la gestion de l’état de santé d’éoliennes et de drones
Spherea (formerly Cassidian Test & Services), initiator of the PhD thesis, is a leading provider of Automatic Test Equipment (ATE) solutions for aerospace and military vehicles’ maintenance. The company’s interest in Integrated Vehicle Health Management (IVHM) research is motivated by occurrence of No Fault Found (NFF) events detected by ATE, and determining superfluous maintenance activities and consequently major wastes of time, energy and money. IVHM, through its advanced diagnostics and prognostics capabilities, and integration at enterprise level of vehicle health management could solve NFF events occurring during operational-level maintenance. Nevertheless, IVHM systems proposed so far are most of the times developed and matured empirically, for specific vehicle systems, founded on proprietary concepts, and lacking of consensual structuring principles. This results in a lack of consensus in both the structuring principles of IVHM systems and their Systems Engineering. Today, the challenge is to provide an IVHM modelling framework independent from the type of supported system and usable for IVHM Systems Engineering. Towards such framework, the main contributions developed in this thesis progressively build the foundation and pillars of an IVHM modelling framework. The notion of system of systems drives our first proposal of defining principles of an overall IVHM system. From this system vision, the focus of the thesis is oriented on the vehicle centric function of IVHM as catalyst of maintenance decisions at operational level, having the ability to solve the industrial problems at the genesis of the thesis. The key structuring principles of this function are analysed upon three dimensions (functional dimension, a dimension of abstraction, and distribution between the on-board /on-ground segment), setting the basis of the proposal of a generic modelling framework IVHM, considering both vehicle and enterprise centric functions. This framework is built following a Model-based Systems Engineering (MBSE) approach, supported by SysML. The major contribution of the thesis is the modelling, within the framework of IVHM, of the generic Health Management Module (gHMM), support for integration of diagnostics and prognostics, key processes of health management. The gHMM formalization enables to integrate diagnostics and prognostics not only in the conventional way: from diagnosis to prognosis, but also in an original one: from prognostics to diagnostics with the purpose of reducing ambiguity groups; the latter is backed-up through the proposal of an algorithm for one elementary activities of the gHMM. The gHMM MBSE engineering thus leads to a generic modelling framework, which, by a principle of instantiation, allows the construction of an IVHM system designed for the health management of individual vehicle systems. Towards such particularization, the thesis investigates characteristics impacting selection of appropriate supporting algorithms. This analysis enables to identify ten generic macro-criteria, which are further formalized based on ontologies and used within a multi-criteria based methodology suited for selecting diagnostics and prognostics algorithms for vehicle health management. Finally, the validation protocol of the scientific contributions is proposed, and applied at different scales of implementation in the field of wind turbine and UAV health management

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37

Khawaja, Taimoor Saleem. "A Bayesian least squares support vector machines based framework for fault diagnosis and failure prognosis". Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34758.

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A high-belief low-overhead Prognostics and Health Management (PHM) system is desired for online real-time monitoring of complex non-linear systems operating in a complex (possibly non-Gaussian) noise environment. This thesis presents a Bayesian Least Squares Support Vector Machine (LS-SVM) based framework for fault diagnosis and failure prognosis in nonlinear, non-Gaussian systems. The methodology assumes the availability of real-time process measurements, definition of a set of fault indicators, and the existence of empirical knowledge (or historical data) to characterize both nominal and abnormal operating conditions. An efficient yet powerful Least Squares Support Vector Machine (LS-SVM) algorithm, set within a Bayesian Inference framework, not only allows for the development of real-time algorithms for diagnosis and prognosis but also provides a solid theoretical framework to address key concepts related to classication for diagnosis and regression modeling for prognosis. SVM machines are founded on the principle of Structural Risk Minimization (SRM) which tends to nd a good trade-o between low empirical risk and small capacity. The key features in SVM are the use of non-linear kernels, the absence of local minima, the sparseness of the solution and the capacity control obtained by optimizing the margin. The Bayesian Inference framework linked with LS-SVMs allows a probabilistic interpretation of the results for diagnosis and prognosis. Additional levels of inference provide the much coveted features of adaptability and tunability of the modeling parameters. The two main modules considered in this research are fault diagnosis and failure prognosis. With the goal of designing an efficient and reliable fault diagnosis scheme, a novel Anomaly Detector is suggested based on the LS-SVM machines. The proposed scheme uses only baseline data to construct a 1-class LS-SVM machine which, when presented with online data, is able to distinguish between normal behavior and any abnormal or novel data during real-time operation. The results of the scheme are interpreted as a posterior probability of health (1 - probability of fault). As shown through two case studies in Chapter 3, the scheme is well suited for diagnosing imminent faults in dynamical non-linear systems. Finally, the failure prognosis scheme is based on an incremental weighted Bayesian LS-SVR machine. It is particularly suited for online deployment given the incremental nature of the algorithm and the quick optimization problem solved in the LS-SVR algorithm. By way of kernelization and a Gaussian Mixture Modeling (GMM) scheme, the algorithm can estimate (possibly) non-Gaussian posterior distributions for complex non-linear systems. An efficient regression scheme associated with the more rigorous core algorithm allows for long-term predictions, fault growth estimation with confidence bounds and remaining useful life (RUL) estimation after a fault is detected. The leading contributions of this thesis are (a) the development of a novel Bayesian Anomaly Detector for efficient and reliable Fault Detection and Identification (FDI) based on Least Squares Support Vector Machines , (b) the development of a data-driven real-time architecture for long-term Failure Prognosis using Least Squares Support Vector Machines,(c) Uncertainty representation and management using Bayesian Inference for posterior distribution estimation and hyper-parameter tuning, and finally (d) the statistical characterization of the performance of diagnosis and prognosis algorithms in order to relate the efficiency and reliability of the proposed schemes.

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38

Gorjian, Nima. "Asset health prediction using the explicit hazard model". Thesis, Queensland University of Technology, 2012. https://eprints.qut.edu.au/57314/1/Nima_Gorjian_Jolfaei_Thesis.pdf.

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The ability to estimate the asset reliability and the probability of failure is critical to reducing maintenance costs, operation downtime, and safety hazards. Predicting the survival time and the probability of failure in future time is an indispensable requirement in prognostics and asset health management. In traditional reliability models, the lifetime of an asset is estimated using failure event data, alone; however, statistically sufficient failure event data are often difficult to attain in real-life situations due to poor data management, effective preventive maintenance, and the small population of identical assets in use. Condition indicators and operating environment indicators are two types of covariate data that are normally obtained in addition to failure event and suspended data. These data contain significant information about the state and health of an asset. Condition indicators reflect the level of degradation of assets while operating environment indicators accelerate or decelerate the lifetime of assets. When these data are available, an alternative approach to the traditional reliability analysis is the modelling of condition indicators and operating environment indicators and their failure-generating mechanisms using a covariate-based hazard model. The literature review indicates that a number of covariate-based hazard models have been developed. All of these existing covariate-based hazard models were developed based on the principle theory of the Proportional Hazard Model (PHM). However, most of these models have not attracted much attention in the field of machinery prognostics. Moreover, due to the prominence of PHM, attempts at developing alternative models, to some extent, have been stifled, although a number of alternative models to PHM have been suggested. The existing covariate-based hazard models neglect to fully utilise three types of asset health information (including failure event data (i.e. observed and/or suspended), condition data, and operating environment data) into a model to have more effective hazard and reliability predictions. In addition, current research shows that condition indicators and operating environment indicators have different characteristics and they are non-homogeneous covariate data. Condition indicators act as response variables (or dependent variables) whereas operating environment indicators act as explanatory variables (or independent variables). However, these non-homogenous covariate data were modelled in the same way for hazard prediction in the existing covariate-based hazard models. The related and yet more imperative question is how both of these indicators should be effectively modelled and integrated into the covariate-based hazard model. This work presents a new approach for addressing the aforementioned challenges. The new covariate-based hazard model, which termed as Explicit Hazard Model (EHM), explicitly and effectively incorporates all three available asset health information into the modelling of hazard and reliability predictions and also drives the relationship between actual asset health and condition measurements as well as operating environment measurements. The theoretical development of the model and its parameter estimation method are demonstrated in this work. EHM assumes that the baseline hazard is a function of the both time and condition indicators. Condition indicators provide information about the health condition of an asset; therefore they update and reform the baseline hazard of EHM according to the health state of asset at given time t. Some examples of condition indicators are the vibration of rotating machinery, the level of metal particles in engine oil analysis, and wear in a component, to name but a few. Operating environment indicators in this model are failure accelerators and/or decelerators that are included in the covariate function of EHM and may increase or decrease the value of the hazard from the baseline hazard. These indicators caused by the environment in which an asset operates, and that have not been explicitly identified by the condition indicators (e.g. Loads, environmental stresses, and other dynamically changing environment factors). While the effects of operating environment indicators could be nought in EHM; condition indicators could emerge because these indicators are observed and measured as long as an asset is operational and survived. EHM has several advantages over the existing covariate-based hazard models. One is this model utilises three different sources of asset health data (i.e. population characteristics, condition indicators, and operating environment indicators) to effectively predict hazard and reliability. Another is that EHM explicitly investigates the relationship between condition and operating environment indicators associated with the hazard of an asset. Furthermore, the proportionality assumption, which most of the covariate-based hazard models suffer from it, does not exist in EHM. According to the sample size of failure/suspension times, EHM is extended into two forms: semi-parametric and non-parametric. The semi-parametric EHM assumes a specified lifetime distribution (i.e. Weibull distribution) in the form of the baseline hazard. However, for more industry applications, due to sparse failure event data of assets, the analysis of such data often involves complex distributional shapes about which little is known. Therefore, to avoid the restrictive assumption of the semi-parametric EHM about assuming a specified lifetime distribution for failure event histories, the non-parametric EHM, which is a distribution free model, has been developed. The development of EHM into two forms is another merit of the model. A case study was conducted using laboratory experiment data to validate the practicality of the both semi-parametric and non-parametric EHMs. The performance of the newly-developed models is appraised using the comparison amongst the estimated results of these models and the other existing covariate-based hazard models. The comparison results demonstrated that both the semi-parametric and non-parametric EHMs outperform the existing covariate-based hazard models. Future research directions regarding to the new parameter estimation method in the case of time-dependent effects of covariates and missing data, application of EHM in both repairable and non-repairable systems using field data, and a decision support model in which linked to the estimated reliability results, are also identified.

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39

Patrick-Aldaco, Romano. "A Model Based Framework for Fault Diagnosis and Prognosis of Dynamical Systems with an Application to Helicopter Transmissions". Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/16266.

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The thesis presents a framework for integrating models, simulation, and experimental data to diagnose incipient failure modes and prognosticate the remaining useful life of critical components, with an application to the main transmission of a helicopter. Although the helicopter example is used to illustrate the methodology presented, by appropriately adapting modules, the architecture can be applied to a variety of similar engineering systems. Models of the kind referenced are commonly referred to in the literature as physical or physics-based models. Such models utilize a mathematical description of some of the natural laws that govern system behaviors. The methodology presented considers separately the aspects of diagnosis and prognosis of engineering systems, but a similar generic framework is proposed for both. The methodology is tested and validated through comparison of results to data from experiments carried out on helicopters in operation and a test cell employing a prototypical helicopter gearbox. Two kinds of experiments have been used. The first one retrieved vibration data from several healthy and faulted aircraft transmissions in operation. The second is a seeded-fault damage-progression test providing gearbox vibration data and ground truth data of increasing crack lengths. For both kinds of experiments, vibration data were collected through a number of accelerometers mounted on the frame of the transmission gearbox. The applied architecture consists of modules with such key elements as the modeling of vibration signatures, extraction of descriptive vibratory features, finite element analysis of a gearbox component, and characterization of fracture progression. Contributions of the thesis include: (1) generic model-based fault diagnosis and failure prognosis methodologies, readily applicable to a dynamic large-scale mechanical system; (2) the characterization of the vibration signals of a class of complex rotary systems through model-based techniques; (3) a reverse engineering approach for fault identification using simulated vibration data; (4) the utilization of models of a faulted planetary gear transmission to classify descriptive system parameters either as fault-sensitive or fault-insensitive; and (5) guidelines for the integration of the model-based diagnosis and prognosis architectures into prognostic algorithms aimed at determining the remaining useful life of failing components.

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40

Le, Thanh Trung. "Contribution to deterioration modeling and residual life estimation based on condition monitoring data". Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAT099/document.

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La maintenance prédictive joue un rôle important dans le maintien des systèmes de production continue car elle peut aider à réduire les interventions inutiles ainsi qu'à éviter des pannes imprévues. En effet, par rapport à la maintenance conditionnelle, la maintenance prédictive met en œuvre une étape supplémentaire, appelée le pronostic. Les opérations de maintenance sont planifiées sur la base de la prédiction des états de détérioration futurs et sur l'estimation de la vie résiduelle du système. Dans le cadre du projet européen FP7 SUPREME (Sustainable PREdictive Maintenance for manufacturing Equipment en Anglais), cette thèse se concentre sur le développement des modèles de détérioration stochastiques et sur des méthodes d'estimation de la vie résiduelle (Remaining Useful Life – RUL en anglais) associées pour les adapter aux cas d'application du projet. Plus précisément, les travaux présentés dans ce manuscrit sont divisés en deux parties principales. La première donne une étude détaillée des modèles de détérioration et des méthodes d'estimation de la RUL existant dans la littérature. En analysant leurs avantages et leurs inconvénients, une adaptation d’une approche de l'état de l'art est mise en œuvre sur des cas d'études issus du projet SUPREME et avec les données acquises à partir d’un banc d'essai développé pour le projet. Certains aspects pratiques de l’implémentation, à savoir la question de l'échange d'informations entre les partenaires du projet, sont également détaillées dans cette première partie. La deuxième partie est consacrée au développement de nouveaux modèles de détérioration et les méthodes d'estimation de la RUL qui permettent d'apporter des éléments de solutions aux problèmes de modélisation de détérioration et de prédiction de RUL soulevés dans le projet SUPREME. Plus précisément, pour surmonter le problème de la coexistence de plusieurs modes de détérioration, le concept des modèles « multi-branche » est proposé. Dans le cadre de cette thèse, deux catégories des modèles de type multi-branche sont présentées correspondant aux deux grands types de modélisation de l'état de santé des système, discret ou continu. Dans le cas discret, en se basant sur des modèles markoviens, deux modèles nommés Mb-HMM and Mb-HsMM (Multi-branch Hidden (semi-)Markov Model en anglais) sont présentés. Alors que dans le cas des états continus, les systèmes linéaires à sauts markoviens (JMLS) sont mis en œuvre. Pour chaque modèle, un cadre à deux phases est implémenté pour accomplir à la fois les tâches de diagnostic et de pronostic. A travers des simulations numériques, nous montrons que les modèles de type multi-branche peuvent donner des meilleures performances pour l'estimation de la RUL par rapport à celles obtenues par des modèles standards mais « mono-branche »
Predictive maintenance plays a crucial role in maintaining continuous production systems since it can help to reduce unnecessary intervention actions and avoid unplanned breakdowns. Indeed, compared to the widely used condition-based maintenance (CBM), the predictive maintenance implements an additional prognostics stage. The maintenance actions are then planned based on the prediction of future deterioration states and residual life of the system. In the framework of the European FP7 project SUPREME (Sustainable PREdictive Maintenance for manufacturing Equipment), this thesis concentrates on the development of stochastic deterioration models and the associated remaining useful life (RUL) estimation methods in order to be adapted in the project application cases. Specifically, the thesis research work is divided in two main parts. The first one gives a comprehensive review of the deterioration models and RUL estimation methods existing in the literature. By analyzing their advantages and disadvantages, an adaption of the state of the art approaches is then implemented for the problem considered in the SUPREME project and for the data acquired from a project's test bench. Some practical implementation aspects, such as the issue of delivering the proper RUL information to the maintenance decision module are also detailed in this part. The second part is dedicated to the development of innovative contributions beyond the state-of-the-are in order to develop enhanced deterioration models and RUL estimation methods to solve original prognostics issues raised in the SUPREME project. Specifically, to overcome the co-existence problem of several deterioration modes, the concept of the "multi-branch" models is introduced. It refers to the deterioration models consisting of different branches in which each one represent a deterioration mode. In the framework of this thesis, two multi-branch model types are presented corresponding to the discrete and continuous cases of the systems' health state. In the discrete case, the so-called Multi-branch Hidden Markov Model (Mb-HMM) and the Multi-branch Hidden semi-Markov model (Mb-HsMM) are constructed based on the Markov and semi-Markov models. Concerning the continuous health state case, the Jump Markov Linear System (JMLS) is implemented. For each model, a two-phase framework is carried out for both the diagnostics and prognostics purposes. Through numerical simulations and a case study, we show that the multi-branch models can help to take into account the co-existence problem of multiple deterioration modes, and hence give better performances in RUL estimation compared to the ones obtained by standard "single branch" models

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41

Bennett, Dr Alexander. ""Diagnostic and Prognostic Imaging in Spondyloarthropathy"". Thesis, University of Leeds, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.534424.

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Losik, Len. "Relying on Telemetry for Mission Critical Decisions: Lessons Learned from NASA's Reusable Launch Vehicle for Use on the Air Force's Next Generation Reusable Launch Vehicle". International Foundation for Telemetering, 2012. http://hdl.handle.net/10150/581746.

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ITC/USA 2012 Conference Proceedings / The Forty-Eighth Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2012 / Town and Country Resort & Convention Center, San Diego, California
The U.S. Air Force's next generation reusable booster (NGRSB) offers the opportunity for the Space Command to use intelligent equipment for decision making replacing personnel, increasing safety and mission assurance by removing decisions from program management personnel who may not have had any flight-test experience. Adding intelligence to launch vehicle and spacecraft equipment may include requiring the builder to use a prognostic and health management (PHM) program. The PHM was added to NASA's aircraft programs in 2009 and we have requested NASA HQ and NASA Marshal Space Flight Center adopt the NASA PHM in the procurement contracts used on the new Space Launch Systems, NASA's congressionally mandated replacement for the Space Shuttle. Space Vehicle Program managers often make decisions for on-orbit spacecraft without ever having on-orbit space flight experience. Intelligent equipment would have eliminated the catastrophic failures on the NASA Space Shuttle Challenger and Columbia. These accidents occurred due to the lack of space vehicle subsystem engineering personnel analyzing real-time equipment telemetry presented on strip chart and video data prior to lift off during pre-launch checkout for the Space Shuttle Challenger and the lack of space vehicle real-time equipment telemetry for Columbia. The PHM requires all equipment to include analog telemetry for measuring the equipment performance and usable life determination in real-time and a prognostic analysis completed manually will identify the equipment that will fail prematurely for replacement before launch preventing catastrophic equipment failures that may cause loss of life.

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Wang, Jian. "Aircraft hydraulic power system diagnostic, prognostics and health management". Thesis, Cranfield University, 2012. http://dspace.lib.cranfield.ac.uk/handle/1826/7424.

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This Individual Research Project (IRP) is the extension research to the group design project (GDP) work which the author has participated in his Msc programme. The GDP objective is to complete the conceptual design of a 200-seat, flying wing civil airliner—FW-11. The next generation aircraft design demands higher reliability, safety and maintainability. With the development of the vehicle hydraulic system technology, the equipment and systems become more and more complex, their reliability and maintenance become more difficult for designers, manufacturers and customers. To improve the mission reliability and reduce life cycle cost, there is strong demand for the application of health management technology into airframe system design. In this research, the author introduced diagnostic, prognostic and health management (DPHM) concept into the aircraft hydraulic power system development. As a brand new technology, it is a challenge to apply the DPHM techniques to on-board system. Firstly, an assumed hydraulic power system was designed for FW-11 by the author and used as the case in his IRP research. Then the crucial components and key parameters needed to be monitored were obtained based on Function Hazard Analysis and Failure Modes Effects Analysis of this system. The writer compared a few diagnostic and prognostic methods in detail, and then selected suitable ones for a hydraulic power system. A diagnostic process was applied to the hydraulic power system using a Case-based reasoning (CBR) approach, whilst a hybrid prognostic method was suggested for the system. After that, a diagnostic, prognostic and health management (DPHM) architecture of the hydraulic power system was designed at system level based on the diagnostic and prognostic research. The whole research work provided a general and practical instruction for hydraulic system design by means of DPHM application.

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Rossi, Karin Kneipp Costa. "Doença de Paget de mama : diagnostico e prognostico". [s.n.], 2003. http://repositorio.unicamp.br/jspui/handle/REPOSIP/313341.

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Orientadores: Cesar Cabello dos Santos, Marcelo Alvarenga
Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Ciências Médicas
Made available in DSpace on 2018-11-08T16:48:16Z (GMT). No. of bitstreams: 1 Rossi_KarinKneippCosta_M.pdf: 728364 bytes, checksum: 48c21ce9f62c6f7360b70a08757cd534 (MD5) Previous issue date: 2003
Resumo: Objetivos: Calcular a frequência, descrever aspectos clínicos, mamográficos, e histopatológicos, assim como formas do tratamento cirúrgico e o prognóstico de pacientes com doença de Paget de mama. Sujeitos e método: Foi realizado um estudo descritivo analítico entre os anos de 1988 a 2002 em mulheres tratadas de câncer de mama no Centro de Atenção Integral à Saúde da Mulher da Universidade Estadual de Campinas. Foram calculados os valores de frequência da doença, descritas características epidemiológicas, do exame clínico, das mamografias, além de formas de tratamento cirúrgico e o prognóstico destas pacientes. Para a análise estatística foram calculados valores de frequência e descritas as porcentagens das categorias das variáveis estudadas. Foram utilizados os testes qui-quadrado, t'student e exato de Fisher para análise de associação de algumas variáveis. Para avaliar o prognóstico da doença foram realizadas curvas de sobrevivência pelo método de Kaplan Mayer analisadas pelo teste LogRank. Resultados: Foram observados 4.536 casos de câncer de mama, sendo que destes, 80 apresentavam doença de Paget de mama. Isto correspondeu no período estudado a uma frequência média de 1,76%. As pacientes assintomáticas em relação ao complexo aréolo-papilar corresponderam a 46% dos casos estudados. Encontraram-se alterações no complexo aréolo-papilar ao exame clínico em 63% das pacientes e estavam associadas a nódulos mamários em mais da metade. A mamografia falhou em diagnosticar os tumores mamários em 30% e em 64% não mostrou alterações no complexo aréolo-papilar. Todos os casos associaram-se com carcinomas mamários subjacentes. Mulheres com sintomas de alterações de complexo aréolo-papilar apresentaram 44% de carcinoma in situ e 66% de axila negativa, sendo que entre aquelas sem sintomas, a freqüência foi de 3% e 34%, respectivamente (p=0,011 e p=0,000). A sobrevida global foi significativamente maior nas pacientes com sintomas no complexo aréolo-papilar do que naquelas sem sintomas (p=0,031). A presença de nodulação ao exame clínico associou-se à presença de carcinoma invasor em 89% e de axila comprometida em 61%, enquanto que a ausência de nodulação associou-se com 38% e 19% respectivamente (p=0,000). As recidivas locais ocorreram em 38% (5/13) das pacientes submetidas a quadrantectomias e em 7,7% (5/65) das mastectomizadas (p=0,002). Conclusão: A doença de Paget de mama foi uma entidade rara. Em quase metade das pacientes não manifestou sintomas no complexo aréolo-papilar. Grande parte das pacientes apresentou ao exame clínico alterações de complexo aréolo-papilar associadas a nodulações nas mamas, sendo que o achado clínico de nódulo esteve associado à doença mais avançada. O exame mamográfico foi inadequado para avaliação do complexo aréolo-papilar. As pacientes submetidas às quadrantectomias apresentaram alta taxa de recorrência local. Os exames clínicos e mamográficos não foram capazes de prever as recorrências locais no grupo das quadrantectomia. As pacientes com queixa de alterações de papila tiveram melhor sobrevida
Abstract: Objetive: To calculate the frequency, and describe the clinical, mamographics and the histopatologic aspects of Paget disease of the breast. Futhermore evalute the surgical treatment and the prognosis of these patients. Method: It was done a descriptive study in breast cancer women attended at Integral Women Attention Center (CAISM) of the University of Campinas between 1988 and 2002. It was calculated the disease frequency and It was performed the associated analyses of some clinical, variables with the Chi-square , t'student and e the FISHER's exat test. It was done Kaplan Mayer curves and LogRank analyse to evaluet the prognosis of the Paget disease of the breast with regards to some aspects. Results: The mean frequency was 1.76%. 46% of patients was assimptomatic about the nipple-areola complex. 63% presented nipple-areola complex disturbed associated to breast lumps generally. The mammography failed the breast tumor associated diagnosis in 30% and in 64% the nipple-areola complex was wrongly normal. All of the cases was associated to breast carcinomas beneth. The nipple-areola complex complains was associated to 44% of in situ ductal carcinomas and 66% of negative axila findings, while the noncomplains women with 3% and 34% respectivelly (p=0.011 and p=0.000). The women with nipple-areola complex complains were associated to better survival compared to the non-complains ones (p=0.031). The breast finding of lump was associated to 89% of invasive breast cancer and 61% of positive axilar node, compared to the non-breast lump patients with 38% and 19% respectivelly (p=0.000). The quandrantectomies was involved to 39% of local relapses compared to the mastectomies with 7.7% (p=0.002). Conclusion: The Paget disease of the breast was rare one. It was not observed nipple-areola complex symptoms in almost half of patients. In the majority of patients was observed finding of nipple-areola complex at clinical breast exam associated to breat lumpness. These clinical finding of breast lump was associated to more advanced breast cancer. The mammography was inappropiate to evaluate the nipple-areola complex. The patients submitted to the quandrantectomies presented high local relapse rate. The clinical and mammographic evaluation were inappropriate to identify the risk of local relapse in the conservative group. The women with nipple-areola complex complains was associated to better survival
Mestrado
Tocoginecologia
Mestre em Tocoginecologia

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Nieto, Mark E. "Naval aviation aging wiring : prognostic and diagnostic solutions /". Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2000. http://handle.dtic.mil/100.2/ADA387353.

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Thesis (M.S. in Management) Naval Postgraduate School, Dec. 2000.
Thesis advisor(s): Eaton, Donald ; Kang, Keebom. "December 2000." Includes bibliographical references (p. 61-63). Also available online.

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46

Tsim, Selina. "Diagnostic and prognostic biomarkers of malignant pleural mesothelioma". Thesis, University of Glasgow, 2018. http://theses.gla.ac.uk/30687/.

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Malignant Pleural Mesothelioma (MPM) is an aggressive intrathoracic malignancy with an overall poor prognosis. MPM is associated with asbestos exposure but has a long latency period between exposure and disease development. Incidence of MPM in the UK is therefore still rising, predicted to reach a peak in 2020. The majority of patients with MPM present with breathlessness, frequently due to a pleural effusion and/or chest pain. Diagnosis of MPM can be difficult. Radiological detection of early stage MPM in particular can be challenging, as pleural tumour, nodularity or significant pleural thickening may not be evident. Diagnosis is further complicated by the low yield of pleural fluid cytology examination in MPM and pleural biopsy is therefore usually required to allow definitive diagnosis. This can be achieved under image guidance, at surgical thoracoscopy or at local anaesthetic thoracoscopy (LAT). A significant number of patients are either elderly or have co-morbidity precluding general anaesthesia and surgical thoracoscopy. Image-guided pleural biopsy is not always feasible, particularly in the absence of significant pleural thickening. LAT remains a limited resource in the UK. A non-invasive biomarker of MPM, which could be performed early in the patient’s presentation, and that could be available to most hospitals, would therefore be a major clinical advance, allowing clinicians to direct appropriate patients to specialist centres with access to LAT and specialist MDT input where MPM appears likely. There have been several potential blood biomarkers identified in the mesothelioma literature, including the most widely studied, Mesothelin, and more recently Fibulin-3 and SOMAscanTM. Unfortunately study results have been variably limited by retrospective study design, inconsistent sampling time points, inconsistent results and lack of external validation, therefore despite initial promising results, none of these biomarkers have entered routine clinical practice for diagnosis. Similarly, utility of imaging biomarkers such as perfusion Computed Tomography (CT), Positron Emission Tomography (PET) and Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) has been limited by high radiation dose, limited availability, and requirement for bulky (and therefore late stage) disease for assessment respectively. In chapter 2, study design, recruitment and preliminary results of the DIAPHRAGM (Diagnostic and Prognostic Biomarkers in the Rational Assessment of Mesothelioma) study are reported. A prospective, multi-centre study was designed, recruiting patients with suspected pleural malignancy (SPM) at initial presentation to secondary care services, from a mixture of academic and more clinical units in the UK and Ireland, in addition to asbestos-exposed control subjects. In one of the largest biomarker studies in mesothelioma to date, 639 patients with SPM and 113 asbestos-exposed control subjects were recruited over three years. Data cleaning is being finalised by the Cancer Research UK Clinical Trials Unit Glasgow at the time of writing. Preliminary results reveal that 26% (n=154) patients recruited to the SPM cohort were diagnosed with MPM, 33% (n=209) had secondary pleural malignancy and 34% (n=218) were diagnosed with benign pleural disease. A final diagnosis is awaited in 7% (n=47) at the time of writing. SOMAscanTM and Fibulin-3 biomarker analyses are ongoing and DIAPHRAGM will definitively answer the question of diagnostic utility of these blood biomarkers in routine clinical practice, in a ‘real-life’ MPM population, relative to that of Mesothelin. In chapter 3, contrast-enhanced MRI was performed in patients with suspected MPM and a novel MRI biomarker of pleural malignancy defined (Early Contrast Enhancement – ECE). ECE was defined as a peak in pleural signal intensity at or before 4.5 minutes after intravenous Gadobutrol administration. ECE assessment was successfully performed in all patients who underwent contrast-enhanced MRI. This included patients with pleural thickening < 10mm (49/58 (84%)), the mean pleural thickness of all patients was 5mm. ECE demonstrated good overall diagnostic performance for the detection of pleural malignancy (sensitivity 83% (95% CI 61 – 94), specificity 83% (95% CI 68 - 91%), positive predictive value 68% (95% CI 47 – 84%), negative predictive value 92% (95% CI 78 – 97%)), comparable to morphology assessment at CT morphology and MRI morphology by experienced thoracic radiologists. In addition, ECE demonstrated good reproducibility (inter-observer κ = 0.864), superior to subjective morphology assessment at CT and MRI. Mean signal intensity gradient (MSIG), a marker of patient’s contrast enhancement pattern, correlated with tumour Microvessel Density (MVD) using Factor VII immunostain (Spearman’s rho = 0.43, p=0.02). Additionally, a high MSIG (>0.533AU/s), indicative of high tumour vascularity, was associated with poor median overall survival (12 months vs. 20 months, p=0.047). Staging of MPM represents an additional challenge to clinicians. This is due to the complex morphology and often rind-like growth pattern of MPM. In addition, delineation of pleural disease from adjacent structures such as intercostal muscle and diaphragm can be difficult to assess, particularly at CT, which is the most commonly used imaging modality for diagnostic and staging assessment in MPM. Current clinical staging frequently underestimates extent of disease, with a significant proportion of patients being upstaged at time of surgery, and is limited by high inter-observer variability. Recent studies have reported the prognostic significance of CT-derived tumour volume; however, many of these studies have been limited by the laborious or complex nature of tumour segmentation, significant inter-observer variability or challenges encountered in separating pleural tumour from adjacent structures, which are often of similar density. MRI is superior to CT in the detection of invasion of the chest wall and diaphragm in MPM. In Chapter 4, MRI was used to quantitatively assess pleural tumour volume in 31 patients with MPM using novel semi-automated segmentation methodology. Four different segmentation methodologies, using Myrian® segmentation software were developed and examined. Optimum methodology was defined, based on the accuracy of volume estimates of an MRI phantom, visual-based analysis, intra-observer agreement and analysis time. Using the optimum methodology, there was acceptable error around the MRI phantom volume (3.6%), a reasonable analysis time (approximately 14 minutes), good intra-observer agreement (intra-class correlation coefficient (ICC) 0.875) and excellent inter-observer agreement (ICC 0.962). Patients with a high MRI-estimated tumour volume (≥300cm3) had a significantly poorer median overall survival (8.5 months vs. 20 months) and was a statistically significant prognostic variable on univariate (HR 2.273 (95% CI 1.162 – 4.446), p=0.016) and multi-variate Cox proportional hazards model (HR 2.114 (95% CI 1.046 – 4.270), p=0.037).

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MONTI, SARA. "Novel Diagnostic and Prognostic Approaches to Systemic Vasculitides". Doctoral thesis, Università degli studi di Pavia, 2021. http://hdl.handle.net/11571/1434015.

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Background. The management of giant cell arteritis (GCA) has gone through a number of paradigmatic changes in the last few years, including novel diagnostic approaches and treatment options. Objectives. We aimed at investigating and improving the management of GCA by: (i) assessing the impact of the fast track ultrasonographic clinic (FTA) of the Rheumatology Department, IRCCS Policlinico S. Matteo, University of Pavia on the risk of permanent visual loss and future relapse; (ii) evaluating the role of quantitative ultrasound assessment in terms of diagnostic and prognostic outcomes in GCA in an International study in collaboration with the University of Oxford; (iii) contributing to the update of the European recommendations on the management of large vessel vasculitis (LVV) by leading on the systematic literature review and participating in the recommendations development process. Methods. Patients referred for suspected GCA to the FTA were recruited if a diagnosis of GCA was confirmed. The role of quantitative ultrasound findings data was assessed, in collaboration with the University of Oxford, from the data of a large cohort study (TABUL Study) with the FTA cohort from the University of Pavia as an independent cohort. Quantitative ultrasound data [number of sites with halos, intima-media thickness (IMT), presence of bilateral halos] at the level of the temporal arteries (TA) and axillary arteries (AX) were assessed. Two systematic literature reviews (SLR) were performed by searching MEDLINE, EMBASE and Cochrane CENTRAL library to inform the European League Against Rheumatism (EULAR) update of the recommendations on the management of LVV. Results. The GCA cohort included 160 patients [female 120 (75%), mean age 72.4±8.2 years]. Sixty-three (39.4%) evaluated with FTA, 97 (60.6%) with conventional approach. Since the introduction of FTA the need for TAB reduced by 93%. Median follow-up duration was shorter in the FTA group compared to the conventional one (0.9 vs. 5.0 years; p<0.001). Permanent visual loss (PVL) occurred in 8 (12.7%) FTA patients and 26 (26.8%) conventional ones (p=0.03). During COVID-19 there was a significant increase in the occurrence of PVL (40%) including bilateral blindness despite a regularly operating FTA clinic. Cumulative incidence of relapses and time to first relapse did not change after FTA introduction. Quantitative ultrasound data were evaluated on 135 GCA patients from TABUL [female 92 (68%), age 73±8 years] and 72 patients from the independent cohort [female 33 (46%), age 75±7 years]. The best-fitting CDS model for TAB used maximum IMT and bilaterality of TA and AX halos. The best-fitting clinical model included raised inflammatory markers, polymyalgia rheumatica, headache and ischaemic symptoms. By combining CDS and clinical models a score to calculate the probability of having a positive TAB, given the ultrasonographic and clinical information, was derived. No significant association was found for prediction of clinical outcome at 6 months. The SLRs confirmed the need to urgently refer the patient to a specialised team, including FTA clinics. The main treatment for LVV remain high-dose GC, however, more evidence has been retrieved to support the use of adjunctive immunosuppressants, including novel biologic treatments for GCA. Conclusion. With our studies we have contributed to clarify the role of novel diagnostic approaches to the disease as part of fast track clinics and supported the role of ultrasound as a reliable diagnostic tool and to significantly reduce the risk of permanent blindness. A quantitative ultrasound analysis (extention and degree of vascular involvement) supported by clinical findings is useful to identify patients with a positive biopsy. Relapse rate and LV-complications did not change upon FTA introduction, highlighting the need for better disease activity monitoring and therapeutic strategies.
Background. The management of giant cell arteritis (GCA) has gone through a number of paradigmatic changes in the last few years, including novel diagnostic approaches and treatment options. Objectives. We aimed at investigating and improving the management of GCA by: (i) assessing the impact of the fast track ultrasonographic clinic (FTA) of the Rheumatology Department, IRCCS Policlinico S. Matteo, University of Pavia on the risk of permanent visual loss and future relapse; (ii) evaluating the role of quantitative ultrasound assessment in terms of diagnostic and prognostic outcomes in GCA in an International study in collaboration with the University of Oxford; (iii) contributing to the update of the European recommendations on the management of large vessel vasculitis (LVV) by leading on the systematic literature review and participating in the recommendations development process. Methods. Patients referred for suspected GCA to the FTA were recruited if a diagnosis of GCA was confirmed. The role of quantitative ultrasound findings data was assessed, in collaboration with the University of Oxford, from the data of a large cohort study (TABUL Study) with the FTA cohort from the University of Pavia as an independent cohort. Quantitative ultrasound data [number of sites with halos, intima-media thickness (IMT), presence of bilateral halos] at the level of the temporal arteries (TA) and axillary arteries (AX) were assessed. Two systematic literature reviews (SLR) were performed by searching MEDLINE, EMBASE and Cochrane CENTRAL library to inform the European League Against Rheumatism (EULAR) update of the recommendations on the management of LVV. Results. The GCA cohort included 160 patients [female 120 (75%), mean age 72.4±8.2 years]. Sixty-three (39.4%) evaluated with FTA, 97 (60.6%) with conventional approach. Since the introduction of FTA the need for TAB reduced by 93%. Median follow-up duration was shorter in the FTA group compared to the conventional one (0.9 vs. 5.0 years; p<0.001). Permanent visual loss (PVL) occurred in 8 (12.7%) FTA patients and 26 (26.8%) conventional ones (p=0.03). During COVID-19 there was a significant increase in the occurrence of PVL (40%) including bilateral blindness despite a regularly operating FTA clinic. Cumulative incidence of relapses and time to first relapse did not change after FTA introduction. Quantitative ultrasound data were evaluated on 135 GCA patients from TABUL [female 92 (68%), age 73±8 years] and 72 patients from the independent cohort [female 33 (46%), age 75±7 years]. The best-fitting CDS model for TAB used maximum IMT and bilaterality of TA and AX halos. The best-fitting clinical model included raised inflammatory markers, polymyalgia rheumatica, headache and ischaemic symptoms. By combining CDS and clinical models a score to calculate the probability of having a positive TAB, given the ultrasonographic and clinical information, was derived. No significant association was found for prediction of clinical outcome at 6 months. The SLRs confirmed the need to urgently refer the patient to a specialised team, including FTA clinics. The main treatment for LVV remain high-dose GC, however, more evidence has been retrieved to support the use of adjunctive immunosuppressants, including novel biologic treatments for GCA. Conclusion. With our studies we have contributed to clarify the role of novel diagnostic approaches to the disease as part of fast track clinics and supported the role of ultrasound as a reliable diagnostic tool and to significantly reduce the risk of permanent blindness. A quantitative ultrasound analysis (extention and degree of vascular involvement) supported by clinical findings is useful to identify patients with a positive biopsy. Relapse rate and LV-complications did not change upon FTA introduction, highlighting the need for better disease activity monitoring and therapeutic strategies.

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48

Maddison, John. "Digital image processing for prognostic and diagnostic clinical pathology". Thesis, University of Huddersfield, 2005. http://eprints.hud.ac.uk/id/eprint/22322/.

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When digital imaging and image processing methods are applied to clinical diagnostic and prognostic needs, the methods can be seen to increase human understanding and provide objective measurements. Most current clinical applications are limited to providing subjective information to healthcare professionals rather than providing objective measures. This Thesis provides detail of methods and systems that have been developed both for objective and subjective microscopy applications. A system framework is presented that provides a base for the development of microscopy imaging systems. This practical framework is based on currently available hardware and developed with standard software development tools. Image processing methods are applied to counter optical limitations of the bright field microscope, automating the system and allowing for unsupervised image capture and analysis. Current literature provides evidence that 3D visualisation has provided increased insight and application in many clinical areas. There have been recent advancements in the use of 3D visualisation for the study of soft tissue structures, but its clinical application within histology remains limited. Methods and applications have been researched and further developed which allow for the 3D reconstruction and visualisation of soft tissue structures using microtomed serial histological sections specimens. A system has been developed suitable for this need is presented giving considerations to image capture, data registration and 3D visualisation, requirements. The developed system has been used to explore and increase 3D insight on clinical samples. The area of automated objective image quantification of microscope slides presents the allure of providing objective methods replacing existing objective and subjective methods, increasing accuracy and rsducinq manual burden. One such existing objective test is DNA Image Ploidy which seeks to characterise cancer by the measurement of DNA content within individual cell nuclei, an accepted but manually burdensome method. The main novelty of the work completed lies in the development of an automated system for DNA Image Ploidy measurement, combining methods for automatic specimen focus, segmentation, parametric extraction and the implementation of an automated cell type classification system. A consideration for any clinical image processing system is the correct sampling of the tissue under study. VVhile the image capture requirements for both objective systems and subjective systems are similar there is also an important link between the 3D structures of the tissue. 3D understanding can aid in decisions regarding the sampling criteria of objective tests for as although many tests are completed in the 2D realm the clinical samples are 3D objects. Cancers such as Prostate and Breast cancer are known to be multi-focal, with areas of seeming physically, independent areas of disease within a single site. It is not possible to understand the true 3D nature of the samples using 2D micro-tomed sections in isolation from each other. The 3D systems described in this report provide a platform of the exploration of the true multi focal nature of disease soft tissue structures allowing for the sampling criteria of objective tests such as DNA Image Ploidy to be correctly set. For the Automated DNA Image Ploidy and the 3D reconstruction and visualisation systems, clinical review has been completed to test the increased insights provided. Datasets which have been reconstructed from microtomed serial sections and visualised with the developed 3D system area presented. For the automated DNA Image Ploidy system, the developed system is compared with the existing manual method to qualify the quality of data capture, operational speed and correctness of nuclei classification. Conclusions are presented for the work that has been completed and discussion given as to future areas of research that could be undertaken, extending the areas of study, increasing both clinical insight and practical application.

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49

Lindbäck, Stefan. "Primary HIV-1 infection : diagnostic, prognostic, and therapeutic aspects /". Stockholm, 1999. http://diss.kib.ki.se/1999/91-628-3606-4/.

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Al-Khalili, Faris. "Coronary heart disease in women : diagnostic and prognostic markers /". Stockholm, 2000. http://diss.kib.ki.se/2000/91-628-4092-4/.

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Tesis sobre el tema "Diagnostics and prognostics"

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