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Development Of A Proof Of Principle System For Sensor Fault Detection Isolation And Accommodation Using Bayesian Belief Networks
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Book Synopsis Development of a Proof-of-principle System for Sensor Fault Detection, Isolation and Accommodation Using Bayesian Belief Networks by : Christopher R. Burrell
Download or read book Development of a Proof-of-principle System for Sensor Fault Detection, Isolation and Accommodation Using Bayesian Belief Networks written by Christopher R. Burrell and published by . This book was released on 1999 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Sensor Fault Detection, Isolation, and Accommodation Using Neural Networks, Fuzzy Logic, and Bayesian Belief Networks by : Hrishikesh B. Aradhye
Download or read book Sensor Fault Detection, Isolation, and Accommodation Using Neural Networks, Fuzzy Logic, and Bayesian Belief Networks written by Hrishikesh B. Aradhye and published by . This book was released on 1997 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis 37th AIAA Aerospace Sciences Meeting and Exhibit by :
Download or read book 37th AIAA Aerospace Sciences Meeting and Exhibit written by and published by . This book was released on 1999 with total page 682 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis International Aerospace Abstracts by :
Download or read book International Aerospace Abstracts written by and published by . This book was released on 1999 with total page 1016 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayesian Networks in Fault Diagnosis by : Baoping Cai
Download or read book Bayesian Networks in Fault Diagnosis written by Baoping Cai and published by World Scientific Publishing Company. This book was released on 2019-01-22 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fault diagnosis is useful for technicians to detect, isolate, identify faults, and troubleshoot. Bayesian network (BN) is a probabilistic graphical model that effectively deals with various uncertainty problems. This model is increasingly utilized in fault diagnosis. This unique compendium presents bibliographical review on the use of BNs in fault diagnosis in the last decades with focus on engineering systems. Subsequently, eleven important issues in BN-based fault diagnosis methodology, such as BN structure modeling, BN parameter modeling, BN inference, fault identification, validation, and verification are discussed in various cases. Researchers, professionals, academics and graduate students will better understand the theory and application, and benefit those who are keen to develop real BN-based fault diagnosis system.
Book Synopsis Development of a Fault Detection and Isolation System Using Statistical Analysis by :
Download or read book Development of a Fault Detection and Isolation System Using Statistical Analysis written by and published by . This book was released on 2007 with total page 59 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayesian Belief Networks for Fault Identification in Aircraft Gas Turbines by :
Download or read book Bayesian Belief Networks for Fault Identification in Aircraft Gas Turbines written by and published by . This book was released on 2000 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper describes the methodology for usage of Bayesian Belief Networks (BBNs) in fault detection for aircraft gas turbine engines. First, the basic theory of BBNs is discussed, followed by a discussion on the application of this theory to a specific engine. In particular, the selection of faults and the means by which operating regions for the BBN system are chosen are analyzed. This methodology is then illustrated using the GE CFM56-7 turbofan engine as an example.
Book Synopsis Bayesian Belief Network for Aero Gas Turbine Module and System Fault Isolation by : A. Kadamb
Download or read book Bayesian Belief Network for Aero Gas Turbine Module and System Fault Isolation written by A. Kadamb and published by . This book was released on 2003 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A fault detection method for FADS system based on interval-valued neutrosophic sets, belief rule base, and D-S evidence reasoning by : Qianlei Jia
Download or read book A fault detection method for FADS system based on interval-valued neutrosophic sets, belief rule base, and D-S evidence reasoning written by Qianlei Jia and published by Infinite Study. This book was released on with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fault detection, with the characteristics of strong uncertainty and randomness, has always been one of the research hotspots in the field of aerospace. Considering that devices will inevitably encounter various unknown interference in the process of use, which greatly limits the performance of many traditional fault detection methods. Therefore, the main aim of this paper is to address this problem from the perspective of uncertainty and randomness of measurement signal. In information engineering, interval-valued neutrosophic sets (IVNSs), belief rule base (BRB), and Dempster-Shafer (D-S) evidence reasoning are always characterized by the strong ability in revealing uncertainty, but each has its drawbacks. As a result, the three theories are firstly combined in this paper to form a powerful fault detection algorithm. Besides, a series of innovations are proposed to improve the method, including a new score function based on p-norm for IVNSs and a new approach of calculating the similarity between IVNSs, which are both proved by authoritative prerequisites. To illustrate the effectiveness of the proposed method, flush air data sensing (FADS), a technologically advanced airborne sensor, is adopted in this paper. The aerodynamic model of FADS is analyzed in detail using knowledge of aerodynamics under subsonic and supersonic conditions, meanwhile, the high-precision model is established based on the aerodynamic database obtained from CFD software.
Book Synopsis Developing a Bayesian Belief Network Model for Quantifying the Probability of Software Failure of a Protection System by : Tsong-Lun Chu
Download or read book Developing a Bayesian Belief Network Model for Quantifying the Probability of Software Failure of a Protection System written by Tsong-Lun Chu and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Dynamic Bayesian Network Based Fault Diagnosis on Nonlinear Dynamic Systems by : Jiannian Weng
Download or read book Dynamic Bayesian Network Based Fault Diagnosis on Nonlinear Dynamic Systems written by Jiannian Weng and published by . This book was released on 2013 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Application of Bayesian Belief Networks to System Fault Diagnostics by : Mariapia Lampis
Download or read book Application of Bayesian Belief Networks to System Fault Diagnostics written by Mariapia Lampis and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Fault Detection with Bayesian Network by : Verron Sylvain
Download or read book Fault Detection with Bayesian Network written by Verron Sylvain and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this chapter, we show that a bayesian network can be an efficient way to diagnose a fault in multivariate processes. We have selected two statistical fault detection techniques (the T 2 chart and the MEWMA chart) and we have demonstrated that these charts can be viewed as a discriminant analysis and so can be implemented in a simple bayesian network. As the efficiency of bayesian network for the diagnosis of systems has already been demonstrated (Verron et al., 2006; Verron et al., 2007), the evident outlook of this work is the full study of the use of bayesian network in order to monitor and control a multivariate process (detection and diagnosis in the same network).
Book Synopsis Advanced Detection, Isolation, and Accommodation of Sensor Failures -- Real-time Evaluation by : Walter C. Merrill
Download or read book Advanced Detection, Isolation, and Accommodation of Sensor Failures -- Real-time Evaluation written by Walter C. Merrill and published by . This book was released on 1987 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Validation of the Thermal Challenge Problem Using Bayesian Belief Networks by : Laura Painton Swiler
Download or read book Validation of the Thermal Challenge Problem Using Bayesian Belief Networks written by Laura Painton Swiler and published by . This book was released on 2005 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: The thermal challenge problem has been developed at Sandia National Laboratories as a testbed for demonstrating various types of validation approaches and prediction methods. This report discusses one particular methodology to assess the validity of a computational model given experimental data. This methodology is based on Bayesian Belief Networks (BBNs) and can incorporate uncertainty in experimental measurements, in physical quantities, and model uncertainties. The approach uses the prior and posterior distributions of model output to compute a validation metric based on Bayesian hypothesis testing (a Bayes' factor). This report discusses various aspects of the BBN, specifically in the context of the thermal challenge problem. A BBN is developed for a given set of experimental data in a particular experimental configuration. The development of the BBN and the method for ''solving'' the BBN to develop the posterior distribution of model output through Monte Carlo Markov Chain sampling is discussed in detail. The use of the BBN to compute a Bayes' factor is demonstrated.
Book Synopsis A Bayesian Least Squares Support Vector Machines Based Framework for Fault Diagnosis and Failure Prognosis by : Taimoor Saleem Khawaja
Download or read book A Bayesian Least Squares Support Vector Machines Based Framework for Fault Diagnosis and Failure Prognosis written by Taimoor Saleem Khawaja and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: A high-belief low-overhead Prognostics and Health Management (PHM) system is desired for online real-time monitoring of complex non-linear systems operating in a complex (possibly non-Gaussian) noise environment. This thesis presents a Bayesian Least Squares Support Vector Machine (LS-SVM) based framework for fault diagnosis and failure prognosis in nonlinear, non-Gaussian systems. The methodology assumes the availability of real-time process measurements, definition of a set of fault indicators, and the existence of empirical knowledge (or historical data) to characterize both nominal and abnormal operating conditions. An efficient yet powerful Least Squares Support Vector Machine (LS-SVM) algorithm, set within a Bayesian Inference framework, not only allows for the development of real-time algorithms for diagnosis and prognosis but also provides a solid theoretical framework to address key concepts related to classication for diagnosis and regression modeling for prognosis. SVM machines are founded on the principle of Structural Risk Minimization (SRM) which tends to nd a good trade-o between low empirical risk and small capacity. The key features in SVM are the use of non-linear kernels, the absence of local minima, the sparseness of the solution and the capacity control obtained by optimizing the margin. The Bayesian Inference framework linked with LS-SVMs allows a probabilistic interpretation of the results for diagnosis and prognosis. Additional levels of inference provide the much coveted features of adaptability and tunability of the modeling parameters. The two main modules considered in this research are fault diagnosis and failure prognosis. With the goal of designing an efficient and reliable fault diagnosis scheme, a novel Anomaly Detector is suggested based on the LS-SVM machines. The proposed scheme uses only baseline data to construct a 1-class LS-SVM machine which, when presented with online data, is able to distinguish between normal behavior and any abnormal or novel data during real-time operation. The results of the scheme are interpreted as a posterior probability of health (1 - probability of fault). As shown through two case studies in Chapter 3, the scheme is well suited for diagnosing imminent faults in dynamical non-linear systems. Finally, the failure prognosis scheme is based on an incremental weighted Bayesian LS-SVR machine. It is particularly suited for online deployment given the incremental nature of the algorithm and the quick optimization problem solved in the LS-SVR algorithm. By way of kernelization and a Gaussian Mixture Modeling (GMM) scheme, the algorithm can estimate (possibly) non-Gaussian posterior distributions for complex non-linear systems. An efficient regression scheme associated with the more rigorous core algorithm allows for long-term predictions, fault growth estimation with confidence bounds and remaining useful life (RUL) estimation after a fault is detected. The leading contributions of this thesis are (a) the development of a novel Bayesian Anomaly Detector for efficient and reliable Fault Detection and Identification (FDI) based on Least Squares Support Vector Machines, (b) the development of a data-driven real-time architecture for long-term Failure Prognosis using Least Squares Support Vector Machines, (c) Uncertainty representation and management using Bayesian Inference for posterior distribution estimation and hyper-parameter tuning, and finally (d) the statistical characterization of the performance of diagnosis and prognosis algorithms in order to relate the efficiency and reliability of the proposed schemes.
Book Synopsis Bayesian Belief Networks by : Remco Ronaldus Bouckaert
Download or read book Bayesian Belief Networks written by Remco Ronaldus Bouckaert and published by . This book was released on 1995 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt:
