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Relevant bibliographies by topics / Diagnostics and prognostics

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Academic literature on the topic 'Diagnostics and prognostics'

Author: Grafiati

Published: 30 November 2024

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Journal articles on the topic "Diagnostics and prognostics"

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Thongboonkerd, Visith. "Clinical proteomics: towards diagnostics and prognostics." Blood 109, no. 12 (June 15, 2007): 5075–76. http://dx.doi.org/10.1182/blood-2007-03-081992.

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Coticchia, Christine M., Jiang Yang, and Marsha A. Moses. "Ovarian Cancer Biomarkers: Current Options and Future Promise." Journal of the National Comprehensive Cancer Network 6, no. 8 (September 2008): 795–802. http://dx.doi.org/10.6004/jnccn.2008.0059.

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As more effective, less toxic cancer drugs reach patients, the need for accurate and reliable cancer diagnostics and prognostics has become widely appreciated. Nowhere is this need more dire than in ovarian cancer; here most women are diagnosed late in disease progression. The ability to sensitively and specifically predict the presence of early disease and its status, stage, and associated therapeutic efficacy has the potential to revolutionize ovarian cancer detection and treatment. This article reviews current ovarian cancer diagnostics and prognostics and potential biomarkers that are being studied and validated. Some of the most recent molecular approaches being used to identify genes and proteins are presented, which may represent the next generation of ovarian cancer diagnostics and prognostics.

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Abbasi, Amirhassan, Foad Nazari, and C. Nataraj. "On Modeling of Vibration and Crack Growth in a Rotor for Prognostics." Annual Conference of the PHM Society 12, no. 1 (November 3, 2020): 9. http://dx.doi.org/10.36001/phmconf.2020.v12i1.1193.

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Prognostics and health management (PHM) include comprehensive engineering approaches that evaluate the real-time health condition of an asset and predict its future states under the actual operating conditions. This predictive ability would result in efficient maintenance approaches such as Condition Based Maintenance (CBM) that can set maintenance strategies optimally and reduce the life cycle costs. Diagnostics and Prognostics are two major concepts in PHM. Detection, Isolation and Identification of faults are done by diagnostics while prognostics deals with estimation of future states. Mechanical fatigue phenomenon that causes crack initiation and propagation is considered as a common reason for failure in mechanical parts. Hence, diagnostics and prognostics of the crack initiation and propagation have been the subject of many research papers recently. The current paper presents a diagnostics and prognostics method capable of detecting the crack initiation and propagation in a rotor under cyclic loading. At the first step, the coupled equations of rotor motion and crack growth are obtained. An extended model of Paris–Erdogan equation is used for crack growth modeling. The coupled equations are solved numerically. A set of features are extracted from the dynamic response of the rotor for a range of crack lengths. A dataset is compiled including features of response, operating frequency, crack length and number of cycles remained until reaching the critical crack length. With the objective of generalization of the results, the dataset is used for creating a model using an Artifical Neural Network (ANN). In the trained ANN the inputs are the operating speed and the outputs are the crack length and the remaining useful life (RUL) that address the diagnostics and prognostics objectives, respectively.

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Petronic, Ivana, Milena Markovic, Dragana Cirovic, Dragana Dzamic, Ana Marsavelski, and Gordana Nikolic. "Paralysis plexus brachialis - diagnostics and prognostics protocol." Srpski arhiv za celokupno lekarstvo 132, suppl. 1 (2004): 58–61. http://dx.doi.org/10.2298/sarh04s1058p.

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Traction injuries of the brachial plexus, if obstetrical, are diagnosed immediately upon birth based on clinical features, while the type and the degree of injury are confirmed by neurophysiological examination. In such cases, physical therapy is promptly applied and followed up until the age of three months, when, after consultation with neurosurgeon, either physical therapy is continued or surgery is performed. In traumatic injuries, based on clinical, neurological and neurophysiological findings, necessary surgical or pre- and postoperative physiatric interventions are performed. Timely diagnostics and therapy of brachial plexus injuries, followed by recovery of paralytic muscle motor function, enable motion coordination and prevention of contractures. From 2000-2004, 181 cases of brachial plexus birth trauma and 26 cases of brachial plexus traumatic lesions were diagnosed and treated in our institution. Among patients, there were 107 boys and 74 girls with birth injury of the brachial plexus, and 16 boys and 8 girls with traction injury of the brachial plexus sustained in traffic accident. Physical treatment involved combined thermo-, electro-, and kinesitherapy, with alignment of extremities. Upon completion of any treatment session and clinical and neurophysiological examinations, doctors? consultation determined whether to continue with physical therapy or to perform surgery followed by physical therapy with rehabilitation until achieving the maximal motor recovery. The analysis of results showed that functional and motor recovery was best if therapy was initiated immediately after the obstetrical injury or following the surgery. Therapeutic approach was individualized and depended on the level and degree of lesions. Thus, maximal motor and functional recovery of the injured extremity was achieved, with work therapy and professional orientation of such patients.

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Reuben, Lim Chi Keong, and David Mba. "Diagnostics and prognostics using switching Kalman filters." Structural Health Monitoring: An International Journal 13, no. 3 (February 24, 2014): 296–306. http://dx.doi.org/10.1177/1475921714522844.

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Ng, Kam W. "Integrated Diagnostics and Prognostics of Rotating Machinery." International Journal of Rotating Machinery 5, no. 1 (1999): 35–40. http://dx.doi.org/10.1155/s1023621x99000032.

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This paper provides an overview of current research efforts in integrated diagnostics and prognostics of rotating machinery. Specifically, the following topics are discussed: sensing techniques and sensors; signal detection, identification and extraction; classification of faults; predictive failure models; data/model fusion; information management; and man–machine interface. Technical issues, recommendations, and future research directions are also addressed.

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Kirwan, Alan, Marta Utratna, Michael E. O’Dwyer, Lokesh Joshi, and Michelle Kilcoyne. "Glycosylation-Based Serum Biomarkers for Cancer Diagnostics and Prognostics." BioMed Research International 2015 (2015): 1–16. http://dx.doi.org/10.1155/2015/490531.

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Cancer is the second most common cause of death in developed countries with approximately 14 million newly diagnosed individuals and over 6 million cancer-related deaths in 2012. Many cancers are discovered at a more advanced stage but better survival rates are correlated with earlier detection. Current clinically approved cancer biomarkers are most effective when applied to patients with widespread cancer. Single biomarkers with satisfactory sensitivity and specificity have not been identified for the most common cancers and some biomarkers are ineffective for the detection of early stage cancers. Thus, novel biomarkers with better diagnostic and prognostic performance are required. Aberrant protein glycosylation is well known hallmark of cancer and represents a promising source of potential biomarkers. Glycoproteins enter circulation from tissues or blood cells through active secretion or leakage and patient serum is an attractive option as a source for biomarkers from a clinical and diagnostic perspective. A plethora of technical approaches have been developed to address the challenges of glycosylation structure detection and determination. This review summarises currently utilised glycoprotein biomarkers and novel glycosylation-based biomarkers from the serum glycoproteome under investigation as cancer diagnostics and for monitoring and prognostics and includes details of recent high throughput and other emerging glycoanalytical techniques.

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Baruah, P., and R. B. Chinnam *. "HMMs for diagnostics and prognostics in machining processes." International Journal of Production Research 43, no. 6 (March 15, 2005): 1275–93. http://dx.doi.org/10.1080/00207540412331327727.

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Li Liu, K. P. Logan, D. A. Cartes, and S. K. Srivastava. "Fault Detection, Diagnostics, and Prognostics: Software Agent Solutions." IEEE Transactions on Vehicular Technology 56, no. 4 (July 2007): 1613–22. http://dx.doi.org/10.1109/tvt.2007.897219.

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Sai Sarathi Vasan, Arvind, Bing Long, and Michael Pecht. "Diagnostics and Prognostics Method for Analog Electronic Circuits." IEEE Transactions on Industrial Electronics 60, no. 11 (November 2013): 5277–91. http://dx.doi.org/10.1109/tie.2012.2224074.

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Dissertations / Theses on the topic "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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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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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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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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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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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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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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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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Books on the topic "Diagnostics and prognostics"

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Rajamani, Ravi. Diagnostics and Prognostics of Aerospace Engines. Warrendale, PA: SAE International, 2018. http://dx.doi.org/10.4271/0768093082.

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Kadry, Seifedine. Diagnostics and prognostics of engineering systems: Methods and techniques. Hershey, PA: Engineering Science Reference, 2013.

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Toms, Allison M., and Amy Fentress, eds. Symposium on In-Service Lubricant and Machine Analysis, Diagnostics, and Prognostics. 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959: ASTM International, 2011. http://dx.doi.org/10.1520/stp1536-eb.

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Peter, Willett, Kirubarajan Thiagalingam, and Society of Photo-optical Instrumentation Engineers., eds. Component and systems diagnostics, prognostics, and health management II: 3-4 April, 2002, Orlando, USA. Bellingham, Wash: SPIE--the International Society for Optical Engineering, 2002.

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International Workshop on Structural Health Monitoring (4th 2003 Stanford, Calif.). Structural health monitoring 2003: From diagnostics & prognostics to structural health management : proceedings of the 4th International Workshop on Structural Health Monitoring, Stanford University, Stanford, CA, September 15-17, 2003. Lancaster, PA: DEStech Pub., 2003.

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Russo, Antonio, Stefano Iacobelli, and Juan Iovanna, eds. Diagnostic, Prognostic and Therapeutic Value of Gene Signatures. Totowa, NJ: Humana Press, 2012. http://dx.doi.org/10.1007/978-1-61779-358-5.

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Nieto, Mark E. Naval aviation aging wiring: Prognostic and diagnostic solutions. Monterey, Calif: Naval Postgraduate School, 2000.

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Murphy, Michael J., ed. Diagnostic and Prognostic Biomarkers and Therapeutic Targets in Melanoma. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-60761-433-3.

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Martinsson, Ulla. Low grade non-Hodgkin lymphomas: Diagnostic and prognostic studies. Uppsala, Sweden: Uppsala University, 1990.

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K, Pantel, ed. Minimal residual epithelial cancer: Diagnostic approaches and prognostic relevance. Stuttgart: Gustav Fischer Verlag, 1996.

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Book chapters on the topic "Diagnostics and prognostics"

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Zhang, Yilu, and Xinyu Du. "Connected Vehicle Diagnostics and Prognostics." In Prognostics and Health Management of Electronics, 479–501. Chichester, UK: John Wiley and Sons Ltd, 2018. http://dx.doi.org/10.1002/9781119515326.ch17.

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Kang, Myeongsu. "Machine Learning: Diagnostics and Prognostics." In Prognostics and Health Management of Electronics, 163–91. Chichester, UK: John Wiley and Sons Ltd, 2018. http://dx.doi.org/10.1002/9781119515326.ch7.

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Ghoshal, Anindya. "Sensor Applications for Structural Diagnostics and Prognostics." In Proceedings of the International Symposium on Engineering under Uncertainty: Safety Assessment and Management (ISEUSAM - 2012), 503–16. India: Springer India, 2012. http://dx.doi.org/10.1007/978-81-322-0757-3_30.

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Marx, Douw G., P. Stephan Heyns, and Stephan Schmidt. "Hybrid Diagnostics and Prognostics of Planetary Gearboxes." In Applied Condition Monitoring, 182–97. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85584-0_19.

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Zhao, Jun, and Matthew W. Lawless. "ncRNA as Diagnostics and Prognostics for Hepatocellular Carcinoma." In MicroRNAs and Other Non-Coding RNAs in Inflammation, 219–30. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13689-9_12.

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Elghaieb, Iheb, Abdelbaki Souid, Ahmed Zouinkhi, and Hedi Sakli. "Defeating Alzheimer's: AI Perspective from Diagnostics to Prognostics." In Machine Learning and Deep Learning Techniques for Medical Image Recognition, 245–56. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003366249-14.

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Xi, Zhimin, Rong Jing, Cheol Lee, and Mushegh Hayrapetyan. "RECENT RESEARCH ON BATTERY DIAGNOSTICS, PROGNOSTICS, AND UNCERTAINTY MANAGEMENT." In Advances in Battery Manufacturing, Service, and Management Systems, 175–216. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119060741.ch8.

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Randall, R. B. "Applications of Spectral Kurtosis in Machine Diagnostics and Prognostics." In Damage Assessment of Structures VI, 21–32. Stafa: Trans Tech Publications Ltd., 2005. http://dx.doi.org/10.4028/0-87849-976-8.21.

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Crowder, James A., John Carbone, and Shelli Friess. "Artificial Neural Diagnostics and Prognostics: Self-Soothing in Cognitive Systems." In Artificial Psychology, 87–98. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17081-3_8.

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Ramirez, Andrea Sanchez, Richard Loendersloot, Tiedo Tinga, and Giuseppe D’Angelo. "Impact Response Characterization as Basis for Bearing Diagnostics and Prognostics." In Proceedings of the 9th IFToMM International Conference on Rotor Dynamics, 567–76. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-06590-8_46.

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Conference papers on the topic "Diagnostics and prognostics"

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Mohr, Fabian, Weike Sun, and Richard D. Braatz. "Advanced Methods in Diagnostics and Prognostics." In 2024 American Control Conference (ACC), 749–62. IEEE, 2024. http://dx.doi.org/10.23919/acc60939.2024.10644895.

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LeFevre, Brian, Treven Baker, Jason Friel, and Nicholos Mackos. "Propulsion System Diagnostic and Reasoning Technology Development." In Vertical Flight Society 70th Annual Forum & Technology Display, 1–11. The Vertical Flight Society, 2014. http://dx.doi.org/10.4050/f-0070-2014-9537.

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The Sikorsky Aircraft Corporation (SAC) and the U.S. Army Aviation Development Directorate (ADD) Aviation Applied Technology Directorate (AATD) are jointly sponsoring the Capability-Based Operations and Sustainment Technology-Aviation (COST-A) project. The objective of COST-A is to demonstrate an integrated set of high-value diagnostics, prognostics, and system health management technologies that reduce the maintenance burden imposed by the Army's rotorcraft fleet, while at the same time preserving safety and reliability. The program was divided into six different primary technical focus areas, with the propulsion system being one of those areas. The primary goal of the COST-A propulsion effort is to develop a robust set of diagnostic and prognostic algorithms that provide insight into the health of key components of a rotorcraft's propulsion system including the main engines and various critical Line Replaceable Units (LRUs). Separate diagnostic algorithms have been developed for key main engine LRUs such as the Hydro-Mechanical Unit (HMU), Anti-Ice / Start Bleed Valve (AISBV), Inlet Particle Separator (IPS) blower, pneumatic starter, and cross-bleed valves. Other diagnostic and prognostic algorithms have been developed or refined that focus on assessing the gas path health of the engine as a whole and of each of its major modules (gas generator and power turbine), and assessing sensor quality to identify biases. In addition, the Auxiliary Power Unit (APU) is the target of both gas path health and sensor quality diagnostics. Information from all of these algorithms is fused using a series of reasoning algorithms which translate condition indicator (CI) data into actionable information for maintenance decision support. In this paper, several of the aforementioned COST-A Propulsion technologies will be discussed in detail and an example failure scenario will be utilized to elaborate diagnostic and reasoner functionality.

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Ruggeri, Massimiliano, Francesco Maita, Luca Maiolo, Mattia Ferri, Christopher Rosi, and Sara Baldoni. "Innovative hyper-thin sensor for cartridge valves diagnostics and prognostics." In 2024 International Maha Fluid Power Conference, 105–20. Denmark: River Publishers, 2024. http://dx.doi.org/10.13052/rp-9788770047456.009.

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Martin, Charles, Alice Murphy, Tsai-Ching Lu, and Steve Slaughter. "Abnormal Derivative Frequency for Sensor and Wiring Prognostics." In Vertical Flight Society 74th Annual Forum & Technology Display, 1–11. The Vertical Flight Society, 2018. http://dx.doi.org/10.4050/f-0074-2018-12775.

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This paper presents a new method for the detection and prognosis of sensor and wiring failures in rotorcraft and aircraft. Our approach, called the Abnormal Derivative Frequency (ADF), is able to detect sensor and wiring failures in the challenging setting where sensor readings do not exceed pre-determined thresholds and thus do not trigger a rotorcraft's onboard diagnostics algorithms to issue warnings, such as fault messages. The ADF is straightforward to compute, which makes it amenable to implementation in onboard mission processor software. We demonstrate the effectiveness of our method on field data collected from hundreds of Apache rotorcraft. We show that the ADF is able to reliably detect the onset of sensor and wiring problems well before they are caught by onboard diagnostic algorithms and maintenance personnel or crew members. Our approach has the added benefit of helping to distinguish between sensor/wiring problems and actual component failures, thus reducing trouble-shooting time and maintenance costs.

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Ma, Xinyi, Jie Liu, Hassan Mahmoud, and Madhav Mishra. "Performance Analysis and Comparison of Pre-Trained CNN in Bearing Fault Diagnostics." In 2024 Prognostics and System Health Management Conference (PHM), 422–27. IEEE, 2024. http://dx.doi.org/10.1109/phm61473.2024.00080.

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Bender, Amelie, Osarenren K. Aimiyekagbon, and Walter Sextro. "Diagnostics and Prognostics for Retrofitted Systems: A Comprehensive Approach for Enhanced System Health Assessment." In 2024 Prognostics and System Health Management Conference (PHM), 159–64. IEEE, 2024. http://dx.doi.org/10.1109/phm61473.2024.00038.

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Orsagh, Rolf F., Jeremy Sheldon, and Christopher J. Klenke. "Prognostics/Diagnostics for Gas Turbine Engine Bearings." In ASME Turbo Expo 2003, collocated with the 2003 International Joint Power Generation Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/gt2003-38075.

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Development of robust in-flight prognostics or diagnostics for oil wetted gas turbine engine components will play a critical role in improving aircraft engine reliability and maintainability. Real-time algorithms for predicting and detecting bearing and gear failures are currently being developed in parallel with emerging flight-capable sensor technologies including in-line oil debris/condition monitors, and vibration analysis MEMS. These advanced prognostic/diagnostic algorithms utilize intelligent data fusion architectures to optimally combine sensor data, with probabilistic component models to achieve the best decisions on the overall health of oil-wetted components. By utilizing a combination of health monitoring data and model-based techniques, a comprehensive component prognostic capability can be achieved throughout a components life, using model-based estimates when no diagnostic indicators are present and monitored features such as oil debris and vibration at later stages when failure indications are detectable. Implementation of these oil-wetted component prognostic modules will be illustrated in this paper using bearing and gearbox test stand run-to-failure data.

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Zaccaria, Valentina, Amare D. Fentaye, and Konstantinos Kyprianidis. "Bayesian Information Fusion for Gas Turbines Diagnostics and Prognostics." In ASME Turbo Expo 2023: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/gt2023-103171.

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Abstract Prognosis, or the forecasting of remaining operational life of a component, is a fundamental step for predictive maintenance of turbomachines. While diagnostics gives important information on the current conditions of the engine, it is through prognostics that a suitable maintenance interval can be determined, which is critical to minimize costs. However, mature prognostic models are still lacking in industry, which still heavily relies on human experience or generic statistical quantifications. Predicting future conditions is very challenging due to many factors that introduce significant uncertainty, including unknown future machine operations, interaction between multiple faults, and inherent errors in diagnostic and prognostic models. Given the importance to quantify this uncertainty and its impact on operational decisions, this work presents an information fusion approach for gas turbine prognostics. Condition monitoring performed by a Bayesian network is fused with a particle filter for prognosis of gas turbine degradation, and the effect of diagnostic models uncertainty on the prognosis are estimated through probabilistic analysis. Gradual and rapid degradation are simulated on a gas turbine performance model and the impact of sensor noise and initial conditions for the particle filter estimation are assessed. This work demonstrates that the combination of Bayesian networks and particle filters can give good results for short-term prognosis.

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Jaw, Link C., and William Wang. "Mathematical Formulation of Model-Based Methods for Diagnostics and Prognostics." In ASME Turbo Expo 2006: Power for Land, Sea, and Air. ASMEDC, 2006. http://dx.doi.org/10.1115/gt2006-90655.

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System health management capability strives to estimate the current and the future operating states of the system through continuous monitoring of key operating variables. Once the current and future states are estimated, this information will be used to help the dispatcher (or field commander) better respond to anticipated fault conditions, it will also help the logistician (or support crew) better prepare for maintenance actions. Estimating the current and the future operating states are commonly referred to as the diagnostic and the prognostic problems. Diagnostic and prognostic problems can be solved by two different approaches: the model-based approach and the data-based approach. The model-based approach is increasingly favored because it is able to provide more accurate estimation of these operating states, and because it is naturally extensible to the physics of failure, which has been considered a promising enhancement to the conventional prognostic technology. To apply the model-based approach more effectively to diagnostic and prognostic problems, we need to cast these problems in a unified formulation to understand the relationship between the two; furthermore, we need to integrate candidate algorithms with the problem formulation to allow sound evaluation of different solution methods. A unified formulation of the diagnostic and prognostic problems has been suggested in a paper presented at the 2005 ASME World Congress. This formulation uses a linear, time-invariant model as the foundation and expressed diagnostics and prognostics as the same threshold-checking problem. However, the formulation does not include the algorithms used in system health management. Augmenting the problem formulation with health management algorithms is the goal of this paper. This paper presents integrated mathematical formulations for several model-based methods to solve the diagnostic and prognostic problems. Each integrated formulation combines the basic problem formulation with a specific algorithm. The set of integrated formulation provides a theoretical perspective of system health management challenges.

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Scott, Andrew. "Electro-mechanical diagnostics/prognostics." In 2007 IEEE Autotestcon. IEEE, 2007. http://dx.doi.org/10.1109/autest.2007.4374239.

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Reports on the topic "Diagnostics and prognostics"

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Nenadic, Nenad G. Gear Fatigue Diagnostics and Prognostics. Fort Belvoir, VA: Defense Technical Information Center, January 2013. http://dx.doi.org/10.21236/ada577427.

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Vance, David, Francois Koenig, and Russell Harris. Prognostics and Diagnostics Program, Tilt Demonstration. Fort Belvoir, VA: Defense Technical Information Center, June 2006. http://dx.doi.org/10.21236/ada450039.

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Johnson, Leslie D., and Terri A. Merdes. Accelerated Capabilities Initiative Condition-Based Maintenance: Machinery Diagnostics/Prognostics II. Fort Belvoir, VA: Defense Technical Information Center, February 2005. http://dx.doi.org/10.21236/ada430339.

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Stewart, Paul. Sensor Fusion, Prognostics, Diagnostics and Failure Mode Control for Complex Aerospace Systems. Fort Belvoir, VA: Defense Technical Information Center, October 2010. http://dx.doi.org/10.21236/ada535693.

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Tom, Kwok F. A Primer on Vibrational Ball Bearing Feature Generation for Prognostics and Diagnostics Algorithms. Fort Belvoir, VA: Defense Technical Information Center, March 2015. http://dx.doi.org/10.21236/ada614145.

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Weiss, Brian A., Donnie Alonzo, and Steve D. Weinman. Summary report on a workshop on advanced monitoring, diagnostics, and prognostics for manufacturing operations. Gaithersburg, MD: National Institute of Standards and Technology, November 2017. http://dx.doi.org/10.6028/nist.ams.100-13.

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Weiss, Brian A., Michael Brundage, Yannick Tamm, Tommi Makila, and Joan Pellegrino. Summary report on the industry forum for monitoring, diagnostics, and prognostics for manufacturing operations. Gaithersburg, MD: National Institute of Standards and Technology, April 2019. http://dx.doi.org/10.6028/nist.ams.100-23.

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Wu, Bin, Lixia Guo, Kaikai Zhen, and Chao Sun. Diagnostic and prognostic value of miRNAs in hepatoblastoma: A systematic review with meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2021. http://dx.doi.org/10.37766/inplasy2021.11.0045.

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Review question / Objective: Background and aim: Increasing evidence has revealed the valuable diagnostic and prognostic applications of dysregulated microRNAs (miRNAs) in hepatoblastoma (HB), the most common hepatic malignancy during childhood. However, these results are inconsistent and remain to be elucidated. In the present study, we aimed to systematically compile up-to-date information regarding the clinical value of miRNAs in HB. Methods: Articles concerning the diagnostic and prognostic value of single miRNAs for HB were searched from databases. The sensitivity (SEN), specificity (SPE), positive and negative likelihood ratios (PLR and NLR), diagnostic odds ratio (DOR), area under the curve (AUC), and hazard ratios (HRs) were separately pooled to explore the diagnostic and prognostic performance of miRNA. Subgroup and meta-regression analyses were further carried out only in the event of heterogeneity. Results: In all, 20 studies, involving 264 HB patients and 206 healthy individuals, met the inclusion criteria in the six included literature articles. For the diagnostic analysis of miRNAs in HB, the pooled SEN and SPE were 0.76 (95% CI: 0.72–0.80) and 0.75 (95% CI: 0.70–0.80), respectively. Moreover, the pooled PLR was 2.79 (95% CI: 2.12–3.66), NLR was 0.34 (95% CI: 0.26–0.45), DOR was 10.24 (95% CI: 6.55–16.00), and AUC was 0.83, indicating that miRNAs had moderate diagnostic value in HB. For the prognostic analysis of miRNAs in HB, the abnormal expressions of miR-21, miR-34a, miR-34b, miR-34c, miR-492, miR-193, miR-222, and miR-224 in patients were confirmed to be associated with a worse prognosis. The pooled HR was 1.74 (95% CI: 1.20–2.29) for overall survival (OS) and 1.74 (95% CI: 1.31–2.18) for event-free survival (EFS), suggesting its potential as a prognostic indicator for HB. Conclusion: To the best of our knowledge, this is the first comprehensive systematic review and meta-analysis that examines the diagnostic and prognostic role of dysregulated miRNAs in HB patients. The combined meta-analysis results supported the previous individual finds that miRNAs might provide a new, noninvasive method for the diagnostic and prognostic analyses ofHB.

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Cassady, C. R., Heather L. Nachtmann, Edward A. Pohl, Alejandro Mendoza, Letitia Pohl, and Nick Rew. Maintenance Decision-Making Under Prognostic and Diagnostic Uncertainty. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada452058.

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Agarwal, Vivek, Nancy J. Lybeck, and Binh T. Pham. Diagnostic and Prognostic Models for Generator Step-Up Transformers. Office of Scientific and Technical Information (OSTI), September 2014. http://dx.doi.org/10.2172/1166054.

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