Relevant bibliographies by topics / Advanced diagnostic model

Academic literature on the topic 'Advanced diagnostic model'

Author: Grafiati
Published: 10 March 2023

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Journal articles on the topic "Advanced diagnostic model"

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OMOTOSHO, LAWRENCE, KEHINDE SOTONWA, BENJAMIN ADEGOKE, OLUWASHINA OYENIRAN, and JOSHUA OYENIYI. "AN AUTOMATED SKIN DISEASE DIAGNOSTIC SYSTEM BASED ON DEEP LEARNING MODEL." Journal of Engineering Studies and Research 27, no. 3 (January 10, 2022): 43–50. http://dx.doi.org/10.29081/jesr.v27i3.287.

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The use of computer technology has significantly advanced the medical sector, and many computer technologies have been used to develop healthcare, such as the patient management system, monitoring and control systems, and diagnostic systems. Technological advances in healthcare have also helped in saving numerous patients and are constantly improving our quality of life. Technology in the medical sector has also had a major effect on almost all healthcare professional techniques and practices. In order to facilitate rapid diagnosis and treatment of different skin diseases by the use of a deep learning model, this study developed a comprehensive framework to improve the decision-making of dermatologists in Nigeria in terms of the diagnosis of selected skin diseases. The developed system achieved the network accuracy of 98.44 % and the validation accuracy of the test set is 99.44 % as specified by the training results, further testing reveal that the developed system yielded rejection rate of 2.2 % and recognition accuracy of 97.8 %.
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Kern, Bastian, and Patrick Jöckel. "A diagnostic interface for the ICOsahedral Non-hydrostatic (ICON) modelling framework based on the Modular Earth Submodel System (MESSy v2.50)." Geoscientific Model Development 9, no. 10 (October 13, 2016): 3639–54. http://dx.doi.org/10.5194/gmd-9-3639-2016.

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Abstract. Numerical climate and weather models have advanced to finer scales, accompanied by large amounts of output data. The model systems hit the input and output (I/O) bottleneck of modern high-performance computing (HPC) systems. We aim to apply diagnostic methods online during the model simulation instead of applying them as a post-processing step to written output data, to reduce the amount of I/O. To include diagnostic tools into the model system, we implemented a standardised, easy-to-use interface based on the Modular Earth Submodel System (MESSy) into the ICOsahedral Non-hydrostatic (ICON) modelling framework. The integration of the diagnostic interface into the model system is briefly described. Furthermore, we present a prototype implementation of an advanced online diagnostic tool for the aggregation of model data onto a user-defined regular coarse grid. This diagnostic tool will be used to reduce the amount of model output in future simulations. Performance tests of the interface and of two different diagnostic tools show, that the interface itself introduces no overhead in form of additional runtime to the model system. The diagnostic tools, however, have significant impact on the model system's runtime. This overhead strongly depends on the characteristics and implementation of the diagnostic tool. A diagnostic tool with high inter-process communication introduces large overhead, whereas the additional runtime of a diagnostic tool without inter-process communication is low. We briefly describe our efforts to reduce the additional runtime from the diagnostic tools, and present a brief analysis of memory consumption. Future work will focus on optimisation of the memory footprint and the I/O operations of the diagnostic interface.
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Blokh, D., N. Zurgil, I. Stambler, E. Afrimzon, Y. Shafran, E. Korech, J. Sandbank, and M. Deutsch. "An Information-theoretical Model for Breast Cancer Detection." Methods of Information in Medicine 47, no. 04 (2008): 322–27. http://dx.doi.org/10.3414/me0440.

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Summary Objectives: Formal diagnostic modeling is an important line of modern biological and medical research. The construction of a formal diagnostic model consists of two stages: first, the estimation of correlation between model parameters and the disease under consideration; and second, the construction of a diagnostic decision rule using these correlation estimates. A serious drawback of current diagnostic models is the absence of a unified mathematical methodological approach to implementing these two stages. The absence of aunified approach makesthe theoretical/biomedical substantiation of diagnostic rules difficult and reduces the efficacyofactual diagnostic model application. Methods: The present study constructs a formal model for breast cancer detection. The diagnostic model is based on information theory. Normalized mutual information is chosen as the measure of relevance between parameters and the patterns studied. The “nearest neighbor” rule is utilized for diagnosis, while the distance between elements is the weighted Hamming distance. The model concomitantly employs cellular fluorescence polarization as the quantitative input parameter and cell receptor expression as qualitative parameters. Results: Twenty-four healthy individuals and 34 patients (not including the subjects analyzed for the model construction) were tested by the model. Twenty-three healthy subjects and 34 patients were correctly diagnosed. Conclusions: The proposed diagnostic model is an open one,i.e.it can accommodate new additional parameters, which may increase its effectiveness.
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Zhu, Ning, Xiaoliang Xing, Limei Cao, Yingjun Zhang, Ti Zhang, Zhen Li, Fen Zou, and Qing Li. "Study on the Diagnosis of Gastric Cancer by Magnetic Beads Extraction and Mass Spectrometry." BioMed Research International 2020 (August 5, 2020): 1–8. http://dx.doi.org/10.1155/2020/2743060.

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Objective. This study constructed a model for the early diagnosis of gastric cancer by comparing the serum peptides profiles of patients with advanced gastric cancer and healthy people. And that model may be the potential to be applied for the efficacy evaluation and recurrence monitoring in gastric cancer. Methods. Serums of 30 healthy people and 30 advanced gastric cancer patients were matched by age and gender were collected. The serum peptide spectrum was obtained by MB-WCX concentration and MALDI-TOF MS analysis. Based on the analysis of the efficiency of differential peptides in the diagnosis of gastric cancer, we first established a model for the diagnosis of gastric cancer based on differential peptides and then carried out external verification. The diagnostic reliability of this model was further tested by compared with carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9). Results. In this present study, we found the expression of two peptide peaks with a molecular weight of 2863 Da and 2953 Da were significantly increased in gastric cancer serum, while the expression of two peptide peaks with a molecular weight of 1945 Da and 2082 Da were significantly decreased. Depending on the characteristics of peptide expression, we constructed a diagnostic model, we compared the sensitivity and specificity of the model established by 2953 Da/1945 Da, and found this model is significantly higher than CEA and CA19-9. Conclusion. There were some differences in serum peptides profiles between patients with advanced gastric cancer and healthy people. The serum peptide diagnostic models based on 2953 Da and 1945 Da have high diagnostic efficiency for advanced gastric cancer. Our result indicated that this model was well worth further validation for clinical diagnosis.
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Al-Hasani, Maryam, Laith R. Sultan, Hersh Sagreiya, Theodore W. Cary, Mrigendra B. Karmacharya, and Chandra M. Sehgal. "Ultrasound Radiomics for the Detection of Early-Stage Liver Fibrosis." Diagnostics 12, no. 11 (November 9, 2022): 2737. http://dx.doi.org/10.3390/diagnostics12112737.

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Objective: The study evaluates quantitative ultrasound (QUS) texture features with machine learning (ML) to enhance the sensitivity of B-mode ultrasound (US) for the detection of fibrosis at an early stage and distinguish it from advanced fibrosis. Different ML methods were evaluated to determine the best diagnostic model. Methods: 233 B-mode images of liver lobes with early and advanced-stage fibrosis induced in a rat model were analyzed. Sixteen features describing liver texture were measured from regions of interest (ROIs) drawn on B-mode images. The texture features included a first-order statistics run length (RL) and gray-level co-occurrence matrix (GLCM). The features discriminating between early and advanced fibrosis were used to build diagnostic models with logistic regression (LR), naïve Bayes (nB), and multi-class perceptron (MLP). The diagnostic performances of the models were compared by ROC analysis using different train-test sampling approaches, including leave-one-out, 10-fold cross-validation, and varying percentage splits. METAVIR scoring was used for histological fibrosis staging of the liver. Results: 15 features showed a significant difference between the advanced and early liver fibrosis groups, p < 0.05. Among the individual features, first-order statics features led to the best classification with a sensitivity of 82.1–90.5% and a specificity of 87.1–89.8%. For the features combined, the diagnostic performances of nB and MLP were high, with the area under the ROC curve (AUC) approaching 0.95–0.96. LR also yielded high diagnostic performance (AUC = 0.91–0.92) but was lower than nB and MLP. The diagnostic variability between test-train trials, measured by the coefficient-of-variation (CV), was higher for LR (3–5%) than nB and MLP (1–2%). Conclusion: Quantitative ultrasound with machine learning differentiated early and advanced fibrosis. Ultrasound B-mode images contain a high level of information to enable accurate diagnosis with relatively straightforward machine learning methods like naïve Bayes and logistic regression. Implementing simple ML approaches with QUS features in clinical settings could reduce the user-dependent limitation of ultrasound in detecting early-stage liver fibrosis.
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Wolff, Jamie K., Michelle Harrold, Tressa Fowler, John Halley Gotway, Louisa Nance, and Barbara G. Brown. "Beyond the Basics: Evaluating Model-Based Precipitation Forecasts Using Traditional, Spatial, and Object-Based Methods." Weather and Forecasting 29, no. 6 (December 1, 2014): 1451–72. http://dx.doi.org/10.1175/waf-d-13-00135.1.

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Abstract While traditional verification methods are commonly used to assess numerical model quantitative precipitation forecasts (QPFs) using a grid-to-grid approach, they generally offer little diagnostic information or reasoning behind the computed statistic. On the other hand, advanced spatial verification techniques, such as neighborhood and object-based methods, can provide more meaningful insight into differences between forecast and observed features in terms of skill with spatial scale, coverage area, displacement, orientation, and intensity. To demonstrate the utility of applying advanced verification techniques to mid- and coarse-resolution models, the Developmental Testbed Center (DTC) applied several traditional metrics and spatial verification techniques to QPFs provided by the Global Forecast System (GFS) and operational North American Mesoscale Model (NAM). Along with frequency bias and Gilbert skill score (GSS) adjusted for bias, both the fractions skill score (FSS) and Method for Object-Based Diagnostic Evaluation (MODE) were utilized for this study with careful consideration given to how these methods were applied and how the results were interpreted. By illustrating the types of forecast attributes appropriate to assess with the spatial verification techniques, this paper provides examples of how to obtain advanced diagnostic information to help identify what aspects of the forecast are or are not performing well.
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Shubina, A. S., and L. M. Petrova. "Training Educational Psychologists: A Model of Working with Diagnostic Case." Psychological-Educational Studies 8, no. 3 (2016): 115–26. http://dx.doi.org/10.17759/psyedu.2016080311.

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The paper describes a model of working with a diagnostic case in educational psychological practice and analyses its compliance with the requirements of the professional standard for educational psychologists as well as with the theoretical bases of psychological assessment as a form of professional activity of a psychologist. The paper reviews the possibilities for making the requirements of the professional standard more specific by means of relating its components to the stages of the diagnostic process. As it is shown, a number of aspects in the diagnostic activity are deficient and require to be specially developed during professional and advanced training. The paper analyses the necessity of designing the content of psychodiagnostic disciplines so that they involve working with diagnostic hypotheses. It also outlines the tasks of mastering psychodiagnostic disciplines which, if solved successfully, would prevent students from making typical diagnostic mistakes. Finally, the paper discusses the difficulties with the development of the gnostic component of diagnostic activity in graduate students with bachelor degrees in a non-psychology field.
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La Spada, Luigi. "Metasurfaces for Advanced Sensing and Diagnostics." Sensors 19, no. 2 (January 16, 2019): 355. http://dx.doi.org/10.3390/s19020355.

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Interest in sensors and their applications is rapidly evolving, mainly driven by the huge demand of technologies whose ultimate purpose is to improve and enhance health and safety. Different electromagnetic technologies have been recently used and achieved good performances. Despite the plethora of literature, limitations are still present: limited response control, narrow bandwidth, and large dimensions. MetaSurfaces, artificial 2D materials with peculiar electromagnetic properties, can help to overcome such issues. In this paper, a generic tool to model, design, and manufacture MetaSurface sensors is developed. First, their properties are evaluated in terms of impedance and constitutive parameters. Then, they are linked to the structure physical dimensions. Finally, the proposed method is applied to realize devices for advanced sensing and medical diagnostic applications: glucose measurements, cancer stage detection, water content recognition, and blood oxygen level analysis. The proposed method paves a new way to realize sensors and control their properties at will. Most importantly, it has great potential to be used for many other practical applications, beyond sensing and diagnostics.
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Sakai, S., K. Kobayashi, J. Nakamura, S. Toyabe, and K. Akazawa. "Accuracy in the Diagnostic Prediction of Acute Appendicitis Based on the Bayesian Network Model." Methods of Information in Medicine 46, no. 06 (2007): 723–26. http://dx.doi.org/10.3414/me9066.

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Summary Objectives : The diagnosis of acute appendicitis is difficult, and a diagnostic error will often lead to either a perforation or the removal of a normal appendix. In this study, we constructed a Bayesian network model for the diagnosis of acute appendicitis and compared the diagnostic accuracy with other diagnostic models, such as the naive Bayes model, an artificial neural network model, and a logistic regression model. Methods : The data from 169 patients, who suffered from acute abdominal pain and who were suspected of having an acute appendicitis, were analyzed in this study. Nine variables were used for the evaluation of the accuracy of the four models for the diagnosis of an acute appendicitis. The naive Bayes model, the Bayesian network model, an artificial neural network model, and a logistic regression model were used i this study for the diagnosis of acute appendicitis. These four models were validated by using the “632 + bootstrap method” for resampling. The levels of accuracy of the four models for diagnosis were compared by the error rates and by the areas under the receiver operating characteristic curves. Results : Through the course of illness, 50.9% (86 of 169) of the patients were diagnosed as having an acute appendicitis. The error rate was the lowest in the Bayesian network model, as compared with the other diagnostic models. The area under the receiver operating characteristic curve analysis also showed that the Bayesian network model provided the most reliable results. Conclusion : The Bayesian network model provided the most accurate results in comparison to other models for the diagnosis of acute appendicitis.
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Schaefer, Inga-Marie, Ronald P. DeMatteo, and César Serrano. "The GIST of Advances in Treatment of Advanced Gastrointestinal Stromal Tumor." American Society of Clinical Oncology Educational Book, no. 42 (April 2022): 1–15. http://dx.doi.org/10.1200/edbk_351231.

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Gastrointestinal stromal tumor (GIST) is the most common malignant neoplasm of mesenchymal origin and a compelling clinical and biologic model for the rational development of molecularly targeted agents. This is because the majority of GISTs are driven by gain-of-function mutations in KIT or PDGFRA receptor tyrosine kinases. Specific GIST mutations circumscribe well-defined molecular subgroups that must be determined during the diagnostic work-up to guide clinical management, including therapeutic decisions. Surgery is the cornerstone treatment in localized disease and can also be clinically relevant in the metastatic setting. The correct combination and sequence of targeted agents and surgical procedures improves outcomes for patients with GIST and should be discussed individually within multidisciplinary expert teams. All currently approved agents for the treatment of GIST are based on orally available tyrosine kinase inhibitors targeting KIT and PDGFRA oncogenic activation. Although first-line imatinib achieves remarkable prolonged disease control, the benefit of subsequent lines of treatment is more modest. Novel therapeutic strategies focus on overcoming the heterogeneity of KIT or PDGFRA secondary mutations and providing more potent inhibition of specific challenging mutations. This article reviews the current understanding and treatment of GIST, with an emphasis on recent advances.
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Dissertations / Theses on the topic "Advanced diagnostic model"

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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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Smith, Ann. "Characterisation of condition monitoring information for diagnosis and prognosis using advanced statistical models." Thesis, University of Huddersfield, 2017. http://eprints.hud.ac.uk/id/eprint/32609/.

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This research focuses on classification of categorical events using advanced statistical models. Primarily utilised to detect and identify individual component faults and deviations from normal healthy operation of reciprocating compressors. Effective monitoring of condition ensuring optimal efficiency and reliability whilst maintaining the highest possible safety standards and reducing costs and inconvenience due to impaired performance. Variability of operating conditions being revealed through examination of vibration signals recorded at strategic points of the process. Analysis of these signals informing expectations with respect to tolerable degrees of imperfection in specific components. Isolating inherent process variability from extraneous variability affords reliable means of ascertaining system health and functionality. Vibration envelope spectra offering highly responsive model parameters for diagnostic purposes. This thesis examines novel approaches to alleviating the computational burdens of large data analysis through investigation of the potential input variables. Three methods are investigated as follows: Method one employs multivariate variable clustering to ascertain homogeneity amongst input variables. A series of heterogeneous groups being formed from each of which explanatory input variables are selected. Data reduction techniques, method two, offer an alternative means of constructing predictive classifiers. A reduced number of reconstructed explanatory variables provide enhanced modelling capabilities ensuring algorithmic convergence. The final novel approach proposed combines both these methods alongside wavelet data compression techniques. Simplifying number of input parameters and individual signal volume whilst retaining crucial information for deterministic supremacy.
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Wang, Ye. "Advances in state estimation, diagnosis and control of complex systems." Doctoral thesis, Universitat Politècnica de Catalunya, 2018. http://hdl.handle.net/10803/669680.

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This dissertation intends to provide theoretical and practical contributions on estimation, diagnosis and control of complex systems, especially in the mathematical form of descriptor systems. The research is motivated by real applications, such as water networks and power systems, which require a control system to provide a proper management able to take into account their specific features and operating limits in presence of uncertainties related to their operation and failures from component malfunctions. Such a control system is expected to provide an optimal operation to obtain efficient and reliable performance. State estimation is an essential tool, which can be used not only for fault diagnosis but also for the controller design. To achieve a satisfactory robust performance, set theory is chosen to build a general framework for descriptor systems subject to uncertainties. Under certain assumptions, these uncertainties are propagated and bounded by deterministic sets that can be explicitly characterized at each iteration step. Moreover, set-invariance characterizations for descriptor systems are also of interest to describe the steady performance, which can also be used for active mode detection. For the controller design for complex systems, new developments of economic model predictive control (EMPC) are studied taking into account the case of underlying periodic behaviors. The EMPC controller is designed to be recursively feasible even with sudden changes in the economic cost function and the closed-loop convergence is guaranteed. Besides, a robust technique is plugged into the EMPC controller design to maintain these closed-loop properties in presence of uncertainties. Engineering applications modeled as descriptor systems are presented to illustrate these control strategies. From the real applications, some additional difficulties are solved, such as using a two-layer control strategy to avoid binary variables in real-time optimizations and using nonlinear constraint relaxation to deal with nonlinear algebraic equations in the descriptor model. Furthermore, the fault-tolerant capability is also included in the controller design for descriptor systems by means of the designed virtual actuator and virtual sensor together with an observer-based delayed controller.
Esta tesis propone contribuciones de carácter teórico y aplicado para la estimación del estado, el diagnóstico y el control óptimo de sistemas dinámicos complejos en particular, para los sistemas descriptores, incluyendo la capacidad de tolerancia a fallos. La motivación de la tesis proviene de aplicaciones reales, como redes de agua y sistemas de energía, cuya naturaleza crítica requiere necesariamente un sistema de control para una gestión capaz de tener en cuenta sus características específicas y límites operativos en presencia de incertidumbres relacionadas con su funcionamiento, así como fallos de funcionamiento de los componentes. El objetivo es conseguir controladores que mejoren tanto la eficiencia como la fiabilidad de dichos sistemas. La estimación del estado es una herramienta esencial que puede usarse no solo para el diagnóstico de fallos sino también para el diseño del control. Con este fin, se ha decidido utilizar metodologías intervalares, o basadas en conjuntos, para construir un marco general para los sistemas de descriptores sujetos a incertidumbres desconocidas pero acotadas. Estas incertidumbres se propagan y delimitan mediante conjuntos que se pueden caracterizar explícitamente en cada instante. Por otra parte, también se proponen caracterizaciones basadas en conjuntos invariantes para sistemas de descriptores que permiten describir comportamientos estacionarios y resultan útiles para la detección de modos activos. Se estudian también nuevos desarrollos del control predictivo económico basado en modelos (EMPC) para tener en cuenta posibles comportamientos periódicos en la variación de parámetros o en las perturbaciones que afectan a estos sistemas. Además, se demuestra que el control EMPC propuesto garantiza la factibilidad recursiva, incluso frente a cambios repentinos en la función de coste económico y se garantiza la convergencia en lazo cerrado. Por otra parte, se utilizan técnicas de control robusto pata garantizar que las estrategias de control predictivo económico mantengan las prestaciones en lazo cerrado, incluso en presencia de incertidumbre. Los desarrollos de la tesis se ilustran con casos de estudio realistas. Para algunas de aplicaciones reales, se resuelven dificultades adicionales, como el uso de una estrategia de control de dos niveles para evitar incluir variables binarias en la optimización y el uso de la relajación de restricciones no lineales para tratar las ecuaciones algebraicas no lineales en el modelo descriptor en las redes de agua. Finalmente, se incluye también una contribución al diseño de estrategias de control con tolerancia a fallos para sistemas descriptores.
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Navicelli, Andrea, Mario Tucci, and Filippo De Carlo. "Analisi ed applicazione di modelli diagnostici e prognostici per guasti e prestazioni di componenti di impianti industriali nell’era I4.0." Doctoral thesis, 2021. http://hdl.handle.net/2158/1234822.

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Il ruolo fondamentale che la manutenzione gioca nei costi di esercizio e nella produttività degli impianti industriali ha portato le aziende e i ricercatori a spostare il loro interesse su questo tema. L'ultima frontiera dell'innovazione in campo manutentivo, resa possibile anche dall'avvento della quarta rivoluzione industriale che promuove la sensorizzazione e l’interconnessione di tutti i macchinari di impianto, è la manutenzione predittiva. Essa mira ad ottenere una previsione accurata della vita utile dei componenti degli impianti industriali al fine di ottimizzare la schedulazione degli interventi sul campo. Lo studio parte da una accurata revisione della letteratura scientifica di settore riguardante le tecniche diagnostiche e prognostiche applicate a componenti di impianti industriali, necessaria alla comprensione dei diversi modelli sviluppati in funzione della tipologia di componente e modo di guasto in analisi. Successivamente ho spostato l’attenzione sul concetto di manutenzione 4.0 al fine di mappare tutte le caratteristiche associate al paradigma dell'Industria 4.0 e le loro possibili applicazioni alla manutenzione. Lo studio condotto ha portato poi alla progettazione, sviluppo e validazione delle metodologie necessarie all’applicazione in real-time di modelli diagnostici e prognostici avanzati, sia statistici che machine learning, necessari all’implementazione sul campo di un sistema di manutenzione predittiva. Grazie all’applicazione delle metodologie proposte ad un caso studio è stato possibile non solo validare i modelli proposti ma anche definire l’architettura informatica necessaria alla loro corretta implementazione sul sistema distribuito di controllo (Distributed Control System - DCS) di impianto in funzione della tipologia del componente e del guasto in analisi. I modelli testati e validati hanno mostrato elevate prestazioni diagnostiche soprattutto per quanto riguarda i modelli ML che sfruttano le Support Vector Machine (SVM). In definitiva, questo lavoro di tesi mostra nel dettaglio tutti i passaggi necessari allo sviluppo di un sistema di manutenzione predittiva efficace in impianto: partendo dall’analisi dei modi di guasto e dalla sensorizzazione dei componenti, passando poi allo sviluppo dei modelli diagnostici e prognostici real-time fino alla costruzione dell’interfaccia di visualizzazione dei risultati delle analisi svolte, analizzando anche l’architettura informatica necessaria al suo corretto funzionamento. The fundamental role that maintenance plays in the operating costs and productivity of industrial plants has led companies and researchers to shift their interest in this issue. The last frontier of innovation in the maintenance field, made possible also by the advent of the fourth industrial revolution which promotes the sensorisation and interconnection of all plant machinery, is predictive maintenance. It aims to obtain an accurate forecast of the useful life of the industrial plants’ components in order to optimise the scheduling of interventions in the field. The study starts from an accurate review of the scientific literature concerning the diagnostic and prognostic techniques applied to industrial plant components, necessary to understand the different models developed according to the type of component and failure mode under analysis. Subsequently I shifted the focus to the maintenance 4.0 concept in order to map all the characteristics associated with the Industry 4.0 paradigm and their possible applications to maintenance operations. The study then led to the design, development and validation of the methodologies necessary for the real-time application of advanced diagnostic and prognostic models, both statistical and machine learning, necessary for the field implementation of a predictive maintenance system. Thanks to the application of the proposed methodologies to a case study, it was possible not only to validate the proposed models but also to define the IT architecture necessary for their correct implementation on the plant's Distributed Control System (DCS) according to the type of component and the fault under analysis. The tested and validated models showed high diagnostic performance, especially regarding the Support Vector Machine (SVM) Machine Learning models. Ultimately, this thesis shows in detail all the steps necessary for the development of an effective predictive maintenance system in the plant: starting from the analysis of failure modes and component sensorisation, then moving on to the development of real-time diagnostic and prognostic models up to the build-up of the interface for visualising the results of the analyses carried out, also analysing the IT architecture necessary for its correct operation.
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Κοψαυτόπουλος, Φώτης. "Advanced functional and sequential statistical time series methods for damage diagnosis in mechanical structures." Thesis, 2012. http://hdl.handle.net/10889/5828.

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The past 30 years have witnessed major developments in vibration based damage detection and identification, also collectively referred to as damage diagnosis. Moreover, the past 10 years have seen a rapid increase in the amount of research related to Structural Health Monitoring (SHM) as quantified by the significant escalation in papers published on this subject. Thus, the increased interest in this engineering field and its associated potential constitute the main motive for this thesis. The goal of the thesis is the development and introduction of novel advanced functional and sequential statistical time series methods for vibration based damage diagnosis and SHM. After the introduction of the first chapter, Chapter II provides an experimental assessment and comparison of vibration based statistical time series methods for Structural Health Monitoring (SHM) via their application on a lightweight aluminum truss structure and a laboratory scale aircraft skeleton structure. A concise overview of the main non-parametric and parametric methods is presented, including response-only and excitation-response schemes. Damage detection and identification are based on univariate (scalar) versions of the methods, while both scalar (univariate) and vector (multivariate) schemes are considered. The methods' effectiveness for both damage detection and identification is assessed via various test cases corresponding to different damage scenarios, multiple experiments and various sensor locations on the considered structures. The results of the chapter confirm the high potential and effectiveness of vibration based statistical time series methods for SHM. Chapter III investigates the identification of stochastic systems under multiple operating conditions via Vector-dependent Functionally Pooled (VFP) models. In many applications a system operates under a variety of operating conditions (for instance operating temperature, humidity, damage location, damage magnitude and so on) which affect its dynamics, with each condition kept constant for a single commission cycle. Typical examples include mechanical structures operating under different environmental conditions, aircrafts under different flight conditions (altitude, velocity etc.), structures under different structural health states (various damage locations and magnitudes). In this way, damage location and magnitude may be considered as parameters that affect the operating conditions and as a result the structural dynamics. This chapter's work is based on the novel Functional Pooling (FP) framework, which has been recently introduced by the Stochastic Mechanical Systems \& Automation (SMSA) group of the Mechanical Engineering and Aeronautics Department of University of Patras. The main characteristic of Functionally Pooled (FP) models is that their model parameters and innovations sequence depend functionally on the operating parameters, and are projected on appropriate functional subspaces spanned by mutually independent basis functions. Thus, the fourth chapter of the thesis addresses the problem of identifying a globally valid and parsimonious stochastic system model based on input-output data records obtained under a sample of operating conditions characterized by more than one parameters. Hence, models that include a vector characterization of the operating condition are postulated. The problem is tackled within the novel FP framework that postulates proper global discrete-time linear time series models of the ARX and ARMAX types, data pooling techniques, and statistical parameter estimation. Corresponding Vector-dependent Functionally Pooled (VFP) ARX and ARMAX models are postulated, and proper estimators of the Least Squares (LS), Maximum Likelihood (ML), and Prediction Error (PE) types are developed. Model structure estimation is achieved via customary criteria (Bayesian Information Criterion) and a novel Genetic Algorithm (GA) based procedure. The strong consistency of the VFP-ARX least squares and maximum likelihood estimators is established, while the effectiveness of the complete estimation and identification method is demonstrated via two Monte Carlo studies. Based on the postulated VFP parametrization a vibration based statistical time series method that is capable of effective damage detection, precise localization, and magnitude estimation within a unified stochastic framework is introduced in Chapter IV. The method constitutes an important generalization of the recently introduced Functional Model Based Method (FMBM) in that it allows, for the first time in the statistical time series methods context, for complete and precise damage localization on continuous structural topologies. More precisely, the proposed method can accurately localize damage anywhere on properly defined continuous topologies on the structure, instead of pre-defined specific locations. Estimator uncertainties are taken into account, and uncertainty ellipsoids are provided for the damage location and magnitude. To achieve its goal, the method is based on the extended class of Vector-dependent Functionally Pooled (VFP) models, which are characterized by parameters that depend on both damage magnitude and location, as well as on proper statistical estimation and decision making schemes. The method is validated and its effectiveness is experimentally assessed via its application to damage detection, precise localization, and magnitude estimation on a prototype GARTEUR-type laboratory scale aircraft skeleton structure. The damage scenarios considered consist of varying size small masses attached to various continuous topologies on the structure. The method is shown to achieve effective damage detection, precise localization, and magnitude estimation based on even a single pair of measured excitation-response signals. Chapter V presents the introduction and experimental assessment of a sequential statistical time series method for vibration based SHM capable of achieving effective, robust and early damage detection, identification and quantification under uncertainties. The method is based on a combination of binary and multihypothesis versions of the statistically optimal Sequential Probability Ratio Test (SPRT), which employs the residual sequences obtained through a stochastic time series model of the healthy structure. In this work the full list of properties and capabilities of the SPRT are for the first time presented and explored in the context of vibration based damage detection, identification and quantification. The method is shown to achieve effective and robust damage detection, identification and quantification based on predetermined statistical hypothesis sampling plans, which are both analytically and experimentally compared and assessed. The method's performance is determined a priori via the use of the analytical expressions of the Operating Characteristic (OC) and Average Sample Number (ASN) functions in combination with baseline data records, while it requires on average a minimum number of samples in order to reach a decision compared to most powerful Fixed Sample Size (FSS) tests. The effectiveness of the proposed method is validated and experimentally assessed via its application on a lightweight aluminum truss structure, while the obtained results for three distinct vibration measurement positions prove the method's ability to operate based even on a single pair of measured excitation-response signals. Finally, Chapter VI contains the concluding remarks and future perspectives of the thesis.
Κατά τη διάρκεια των τελευταίων 30 ετών έχει σημειωθεί σημαντική ανάπτυξη στο πεδίο της ανίχνευσης και αναγνώρισης βλαβών, το οποίο αναφέρεται συνολικά και σαν διάγνωση βλαβών. Επίσης, κατά την τελευταία δεκαετία έχει σημειωθεί σημαντική πρόοδος στον τομέα της παρακολούθησης της υγείας (δομικής ακεραιότητας) κατασκευών. Στόχος αυτής της διατριβής είναι η ανάπτυξη εξελιγμένων συναρτησιακών και επαναληπτικών μεθόδων χρονοσειρών για τη διάγνωση βλαβών και την παρακολούθηση της υγείας κατασκευών υπό ταλάντωση. Αρχικά γίνεται η πειραματική αποτίμηση και κριτική σύγκριση των σημαντικότερων στατιστικών μεθόδων χρονοσειρών επί τη βάσει της εφαρμογής τους σε πρότυπες εργαστηριακές κατασκευές. Εφαρμόζονται μη-παραμετρικές και παραμετρικές μέθοδοι που βασίζονται σε ταλαντωτικά σήματα διέγερσης και απόκρισης των κατασκευών. Στη συνέχεια αναπτύσσονται στοχαστικά συναρτησιακά μοντέλα για την στοχαστική αναγνώριση κατασκευών υπό πολλαπλές συνθήκες λειτουργίας. Τα μοντέλα αυτά χρησιμοποιούνται για την αναπαράσταση κατασκευών σε διάφορες καταστάσεις βλάβης (θέση και μέγεθος βλάβης), ώστε να είναι δυνατή η συνολική μοντελοποίσή τους για όλες τις συνθήκες λειτουργίας. Τα μοντέλα αυτά αποτελούν τη βάση στην οποία αναπτύσσεται μια συναρτησιακή μέθοδος η οποία είναι ικανή να αντιμετωπίσει συνολικά και ενιαία το πρόβλημα της ανίχνευσης, εντοπισμού και εκτίμησης βλαβών σε κατασκευές. Η πειραματική αποτίμηση της μεθόδου γίνεται με πολλαπλά πειράματα σε εργαστηριακό σκελετό αεροσκάφους. Στο τελευταίο κεφάλαιο της διατριβής προτείνεται μια καινοτόμος στατιστική επαναληπτική μέθοδο για την παρακολούθηση της υγείας κατασκευών. Η μέθοδος κρίνεται αποτελεσματική υπό καθεστώς λειτουργικών αβεβαιοτήτων, καθώς χρησιμοποιεί επαναληπτικά και στατιστικά τεστ πολλαπλών υποθέσεων. Η αποτίμηση της μεθόδου γίνεται με πολλαπλά εργαστηριακά πειράματα, ενώ η μέθοδος κρίνεται ικανή να λειτουργήσει με τη χρήση ενός ζεύγους ταλαντωτικών σημάτων διέγερσης-απόκρισης.
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Σπυριδωνάκος, Μηνάς. "Advanced and complete functional series time-dependent ARMA (FS-TARMA) methods for the identification and fault diagnosis of non-stationary stochastic structural systems." Thesis, 2012. http://hdl.handle.net/10889/5829.

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Non-stationary signals, that is signals with time-varying (TV) statistical properties, are commonly encountered in engineering practice. The vibration responses of structures, such as traffic-excited bridges, robotic devices, rotating machinery, and so on, constitute typical examples of non-stationary signals. Structures characterized by properties that vary with time are generally referred as TV structures and their vibration-based identification under normal operating conditions is a significant and challenging problem. An important class of parametric methods for the solution of this problem is based on Functional Series Time-dependent AutoRegressive Moving Average (FS-TARMA) models. These models have parameters that explicitly depend on time, with the dependence described by deterministic functions belonging to specific functional sub-spaces. The focus of the present thesis is on the development of complete and advanced FS-TARMA methods that will offer important improvements in overcoming drawbacks of existent methods and will further foster practical use and application of FS-TARMA models in non-stationary vibration analysis. The specific objectives of the thesis are: a) The introduction of a novel class of Adaptable FS-TARMA (AFS-TARMA) models and the development of a method for their effective identification. AFS-TARMA models are adaptable in the sense that they are not based on basis functions of a fixed form, but instead, they use basis functions with a-priori unknown properties that may adapt to the specific random signal characteristics. b) The postulation of a vector FS-TARMA method for output-only structural identification and the development of effective tools for both model parameter estimation and model structure selection. c) The introduction of a statistical method for vibration-based fault diagnosis in TV structures. d) The presentation of a thorough review on FS-TARMA models covering both theoretical and practical aspects of the model parameter estimation and structure selection problems with special emphasis being placed on promising recent methods. The methods that are developed in each chapter of this thesis are validated through their application in both numerical and experimental case studies and comparisons with currently available non-stationary signal identification methods. The results of the study demonstrate the new methods' applicability, effectiveness, and high potential for parsimonious and accurate identification and dynamic analysis of TV structures.
Μη-στάσιμα σήματα, δηλαδή σήματα με χρονικά μεταβαλλόμενες (ΧΜ) στατιστικές ιδιότητες, απαντώνται συχνά στην επιστήμη του μηχανικού. Τυπικά παραδείγματα αποτελούν οι ταλαντωτικές αποκρίσεις κατασκευών, όπως γέφυρες με κινούμενα οχήματα, ρομποτικές διατάξεις, περιστρεφόμενες μηχανές και άλλες. Κατασκευές που χαρακτηρίζονται από ιδιότητες οι οποίες μεταβάλλονται με τον χρόνο αναφέρονται ως ΧΜ κατασκευές και η δυναμική αναγνώριση και ανάλυση τους επί τη βάση ταλαντωτικών σημάτων απόκρισης αποτελεί σημαντικό και ταυτόχρονα δύσκολο πρόβλημα. Μια σημαντική τάξη παραμετρικών μεθόδων για την επίλυση αυτού του προβλήματος βασίζεται στα συναρτησιακά χρονικά μεταβαλλόμενα μοντέλα αυτοπαλινδρόμησης κινητού μέσου όρου (FS-TARMA, Functional Series Time-Dependent Auto-Regressive Moving Average). Τα μοντέλα αυτά χαρακτηρίζονται απο ΧΜ παραμέτρους οι οποίες ακολουθούν καθοριστικό πρότυπο και κατά συνέπεια μπορούν να προβληθούν σε κατάλληλα επιλεγμένους συναρτησιακούς υποχώρους. Ως βασικός στόχος της παρούσας διατριβής ορίζεται η ανάπτυξη εξελιγμένων μεθόδων μοντελοποίησης FS-TARMA οι οποίες θα προσφέρουν σημαντικές βελτιώσεις στις υπάρχουσες προσεγγίσεις και θα βοηθήσουν στην αντιμετώπιση πρακτικών προβλημάτων που σχετίζονται τόσο με την αναγνώριση των δυναμικών χαρακτηριστικών όσο και την διάγνωση βλαβών σε ΧΜ κατασκευές. Οι συγκεκριμένοι στόχοι της διατριβής μπορούν να περιγραφούν ως ακολούθως: α) Εισαγωγή καινοτόμων προσαρμόσιμων μοντέλων FS-TARMA και ανάπτυξη κατάλληλης μεθόδου για την αποτελεσματική εκτίμηση τους. Τα νέα μοντέλα είναι προσαρμόσιμα υπό την έννοια ότι δεν βασίζονται σε προκαθορισμένες συναρτήσεις βάσης, αλλά αντιθέτως χρησιμοποιούν συναρτήσεις βάσης με εκ των προτέρων άγνωστες ιδιότητες οι οποίες μπορούν να προσαρμοστούν στα χαρακτηριστικά συγκεκριμένου σήματος. β) Ανάπτυξη διανυσματικής μεθόδου εκτίμησης μοντέλων FS-TARMA για την αναγνώριση κατασκευών μέσα από διανυσματικά σήματα ταλαντωτικής απόκρισης. Ανάπτυξη αποδοτικών εργαλείων τόσο για το πρόβλημα εκτίμησης των παραμέτρων όσο και της επιλογής της δομής του μοντέλου. γ) Εισαγωγή στατιστικής μεθόδου για την διάγνωση βλαβών σε ΧΜ κατασκευές μέσω μοντέλων FS-TAR. δ) Παρουσίαση μιας διεξοδικής επισκόπησης των μοντέλων FS-TARMA η οποία καλύπτει τόσο θεωρητικά όσο και πρακτικά ζητήματα των προβλημάτων εκτίμησης των παραμέτρων και επιλογής της δομής των μοντέλων. Η αποτελεσματικότητα των μοντέλων και των μεθόδων που αναπτύσσονται σε κάθε κεφάλαιο αυτής της διατριβής διερευνάται µέσω της εφαρµογής τους τόσο σε αριθµητικές όσο και πειραµατικές µελέτες και συγκρίσεις µε υπάρχουσες µη-στάσιµες µεθόδους αναγνώρισης σηµάτων. Τα αποτελέσματα της εργασίας αυτής επιδεικνύουν την ικανότητα των νέων μοντέλων να παρέχουν εξαιρετικά ακριβείς αναπαραστάσεις ΧΜ κατασκευών κατάλληλων τόσο για την δυναμική ανάλυση όσο και για την διάγνωση βλαβών σε αυτές.
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Books on the topic "Advanced diagnostic model"

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Advances in vascular medicine. London: Springer, 2010.

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Jelali, Mohieddine. Detection and Diagnosis of Stiction in Control Loops: State of the Art and Advanced Methods. London: Springer-Verlag London, 2010.

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NATO Advanced Study Institute on Advanced Modeling Techniques for Rapid Diagnosis and Assessment of CBRN Agents Effects on Water Resources (2005 Istanbul, Turkey). Assessment of the fate and effects of toxic agents on water resources: [proceedings of the NATO Advanced Study Institute on Advanced Modeling Techniques for Rapid Diagnosis and Assessment of CBRN agents effects on water resources, Istanbul, Turkey, 4-16 December 2005]. Dordrecht, the Netherlands: Springer, 2007.

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1935-, Lasker G. E., International Institute for Advanced Studies in Systems Research and Cybernetics., and International Conference on Systems Research, Informatics and Cybernetics (13th : 2001 : Baden-Baden, Germany), eds. Advances in database and expert systems: Data mining and data warehousing techniques, similarity search for reusable database components, performance assessment of learning algorithms, estimation of models for expert systems, complexity evaluation of software processes, multi-agent systems, multi-agent approach to coalition formation, communication between software agents in distributed information systems, expert systems for fault diagnosis, parameter modulated fractals generators, information management in Intranet and Extranet environments. Windsor, Ont: International Institute for Advanced Studies in Systems Research and Cybernetics, 2001.

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Del Giudice, Marco. Evolutionary Psychopathology. Oxford University Press, 2018. http://dx.doi.org/10.1093/med-psych/9780190246846.001.0001.

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This book presents a unified approach to evolutionary psychopathology, and advances an integrative framework for the analysis and classification of mental disorders based on the concepts of life history theory. The framework does not aim to replace existing evolutionary models of specific disorders—which are reviewed and critically discussed in the book—but to connect them in a broader perspective and explain the large-scale patterns of risk and comorbidity that characterize psychopathology. The life history framework permits a seamless integration of mental disorders with normative individual differences in personality and cognition, and offers new conceptual tools for the analysis of developmental, genetic, and neurobiological data. The concepts synthesized in the book are used to derive a new taxonomy of mental disorders, the fast-slow-defense (FSD) model. The FSD model is the first classification system explicitly based on evolutionary concepts, a biologically grounded alternative to transdiagnostic models based on empirical correlations between symptoms. The book reviews a wide range of common mental disorders, discusses their classification in the FSD model, and identifies functional subtypes within existing diagnostic categories.
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Ibrahim, El-Sayed H. Heart Mechanics: Magnetic Resonance Imaging--Advanced Techniques, Clinical Applications, and Future Trends. Taylor & Francis Group, 2017.

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Ibrahim, El-Sayed H. Heart Mechanics: Magnetic Resonance Imaging--Advanced Techniques, Clinical Applications, and Future Trends. Taylor & Francis Group, 2017.

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Ibrahim, El-Sayed H. Heart Mechanics: Magnetic Resonance Imaging--Advanced Techniques, Clinical Applications, and Future Trends. Taylor & Francis Group, 2017.

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Ibrahim, El-Sayed H. Heart Mechanics: Magnetic Resonance Imaging--Advanced Techniques, Clinical Applications, and Future Trends. Taylor & Francis Group, 2017.

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Handler, Clive, Gerry Coghlan, David Abraham, and Michael Dashwood. Advances in Vascular Medicine. Springer, 2014.

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Book chapters on the topic "Advanced diagnostic model"

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Radkowitsch, Anika, Michael Sailer, Martin R. Fischer, Ralf Schmidmaier, and Frank Fischer. "Diagnosing Collaboratively: A Theoretical Model and a Simulation-Based Learning Environment." In Learning to Diagnose with Simulations, 123–41. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89147-3_10.

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AbstractIn their daily practice, physicians with different professional backgrounds often diagnose a patient’s problem collaboratively. In those situations, physicians not only need to be able to diagnose individually, but also need additional collaborative competences such as information sharing and negotiation skills (Liu et al., 2015), which can influence the quality of the diagnostic outcome (Tschan et al., 2009). We introduce the CDR model, a process model for collaborative diagnostic reasoning processes by diagnosticians with different knowledge backgrounds. Building on this model, we develop a simulation in order to assess and facilitate collaborative diagnostic competences among advanced medical students. In the document-based simulation, learners sequentially diagnose five patients by inspecting a health record for symptoms. Then, learners request a radiological diagnostic procedure from a simulated radiologist. By interacting with the simulated radiologist, the learners elicit more evidence for their hypotheses. Finally, learners are asked to integrate all information and suggest a final diagnosis.
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Agarwal, Deepak, and Chandan Kumar Singh. "Model-Based Fault Detection on Modern Automotive Engines." In Advanced Engine Diagnostics, 167–204. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-3275-3_9.

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Bilger, R. W. "Advanced Laser Diagnostics: Implications of Recent Results for Advanced Combustor Models." In Aerothermodynamics in Combustors, 3–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-84755-4_1.

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Streckfus, Charles F., Lenora Bigler, Courtney Edwards, Cynthia Guajardo-Streckfus, and Steven A. Bigler. "Using Saliva Secretions to Model Disease Progression." In Advances in Salivary Diagnostics, 187–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-45399-5_9.

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Struss, Peter. "Diagnosis with Multiple Models for Advanced Applications." In Angewandte Informatik und Software / Applied Computer Science and Software, 201–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-93501-5_17.

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Isermann, Rolf. "Advanced model-based diagnosis of internal combustion engines." In Proceedings, 413–32. Wiesbaden: Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-12918-7_27.

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Kusama, Y. "Requirements for Diagnostics in Controlling Advanced Tokamak Modes." In Advanced Diagnostics for Magnetic and Inertial Fusion, 31–38. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4419-8696-2_5.

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Mozetič, Igor. "Model-based diagnosis: An overview." In Advanced Topics in Artificial Intelligence, 419–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/3-540-55681-8_48.

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Michalak, Marcin, Beata Sikora, and Jurand Sobczyk. "Diagnostic Model for Longwall Conveyor Engines." In Advances in Intelligent Systems and Computing, 437–48. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23437-3_37.

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Simani, Silvio, Cesare Fantuzzi, and Ronald Jon Patton. "Model-Based Fault Diagnosis Techniques." In Advances in Industrial Control, 19–60. London: Springer London, 2003. http://dx.doi.org/10.1007/978-1-4471-3829-7_2.

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Conference papers on the topic "Advanced diagnostic model"

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Benkaci, Mourad, Andrei Doncescu, and Makoto Takizawa. "Conflict Intersection as Diagnostic Model." In 2012 IEEE Workshops of International Conference on Advanced Information Networking and Applications (WAINA). IEEE, 2012. http://dx.doi.org/10.1109/waina.2012.224.

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Roemer, Michael J., and Gregory J. Kacprzynski. "Advanced Diagnostic and Prognostic Technologies for Gas Turbine Engine Risk Assessment." In ASME Turbo Expo 2000: Power for Land, Sea, and Air. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/2000-gt-0030.

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Real-time, integrated health monitoring of gas turbine engines that can detect, classify, and predict developing engine faults is critical to reducing operating and maintenance costs while optimizing the life of critical engine components. Statistical-based anomaly detection algorithms, fault pattern recognition techniques and advanced probabilistic models for diagnosing structural, performance and vibration related faults and degradation can now be developed for real-time monitoring environments. Integration and implementation of these advanced technologies presents a great opportunity to significantly enhance current engine health monitoring capabilities and risk management practices. This paper describes some novel diagnostic and prognostic technologies for dedicated, real-time sensor analysis, performance anomaly detection and diagnosis, vibration fault detection, and component prognostics. The technologies have been developed for gas turbine engine health monitoring and prediction applications which includes an array of intelligent algorithms for assessing the total ‘health’ of an engine, both mechanically and thermodynamically. This includes the ability to account for uncertainties from engine transient conditions, random measurement fluctuations and modeling errors associated with model-based diagnostic and prognostic procedures. The implementation of probabilistic methods in the diagnostic and prognostic methodology is critical to accommodating for these types of uncertainties.
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Kasian, Mykola, and Kostiantyn Kasian. "Diagnostic mathematical model of radio-electronic devices." In 2018 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET). IEEE, 2018. http://dx.doi.org/10.1109/tcset.2018.8336312.

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Kawasaki, Mami, Kazuya Nakano, Takashi Ohnishi, Masashi Sekine, Eizo Watanabe, Shigeto Oda, Taka-aki Nakada, and Hideaki Haneishi. "Motion picture acquisition and analysis of microcirculation in septic model rats." In Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XIX, edited by Caroline Boudoux and James W. Tunnell. SPIE, 2021. http://dx.doi.org/10.1117/12.2581275.

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Li, Ting, Boan Pan, Xiaobo Huang, Weichao Liu, and Xiang Fang. "Which experimental model can sensitively indicate brain death by functional near-infrared spectroscopy?" In Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XVI, edited by Tuan Vo-Dinh, Anita Mahadevan-Jansen, and Warren S. Grundfest. SPIE, 2018. http://dx.doi.org/10.1117/12.2288971.

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Kim, Minji, Yuhua Quan, Byeong Hyun Choi, Yeonho Choi, Hyun Koo Kim, and Beop-Min Kim. "Conditions for NIR fluorescence-guided tumor resectioning in preclinical lung cancer model (Conference Presentation)." In Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XIV, edited by Tuan Vo-Dinh, Anita Mahadevan-Jansen, and Warren S. Grundfest. SPIE, 2016. http://dx.doi.org/10.1117/12.2212494.

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Lee, Hong-Yi, Ping-Hsien Chen, Kuo-Wei Chang, Wen-Chuan Kuo, and Lin Tzu-Han. "Detection of oral early cancerous lesion by using polarization-sensitive optical coherence tomography: mice model." In Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XVI, edited by Tuan Vo-Dinh, Anita Mahadevan-Jansen, and Warren S. Grundfest. SPIE, 2018. http://dx.doi.org/10.1117/12.2291314.

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Kim, Minji, Yuhua Quan, Kook Nam Han, Byeong Hyun Choi, Yeonho Choi, Hyun Koo Kim, and Beop-Min Kim. "NIR fluorescent image-based evaluation of gastric tube perfusion after esophagectomy in preclinical model (Conference Presentation)." In Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XIV, edited by Tuan Vo-Dinh, Anita Mahadevan-Jansen, and Warren S. Grundfest. SPIE, 2016. http://dx.doi.org/10.1117/12.2214064.

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Ross, Weston, Matthew Tucker, Guangshen Ma, and Patrick Codd. "Model for and analysis of intraoperative brain tumor boundary detection based on known spectral signatures of glioblastoma." In Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XVIII, edited by Anita Mahadevan-Jansen. SPIE, 2020. http://dx.doi.org/10.1117/12.2546329.

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Gruner, Michael, Ronny Maschke, Christopher Taudt, Patrick Hoyer, and Peter Hartmann. "Laser vibrometric characterization and model development of a human vocal tract for acoustic therapy of deaf patients." In Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XVIII, edited by Anita Mahadevan-Jansen. SPIE, 2020. http://dx.doi.org/10.1117/12.2546955.

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