Investigation of Bayesian network for reliability analysis and fault diagnosis of complex systems with real case applications

Reliability is critical for complex engineering systems. Traditionally, reliability analysis and fault diagnosis of complex engineering systems is based on reliability block diagram and fault tree. These methods are limited either on the flexibility for system characterization or on the capability f...

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Main Authors: Shen Chen, Zhen Qi, Dehuai Chen, Liangfu Guo, Weiwen Peng
Format: Article
Language:English
Published: SAGE Publishing 2017-10-01
Series:Advances in Mechanical Engineering
Online Access:https://doi.org/10.1177/1687814017728853
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spelling doaj-65216cd480a24e469a44b1a4104e5aa22020-11-25T03:44:12ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402017-10-01910.1177/1687814017728853Investigation of Bayesian network for reliability analysis and fault diagnosis of complex systems with real case applicationsShen Chen0Zhen Qi1Dehuai Chen2Liangfu Guo3Weiwen Peng4China Academy of Engineering Physics, Mianyang, ChinaChina Academy of Engineering Physics, Mianyang, ChinaChina Academy of Engineering Physics, Mianyang, ChinaChina Academy of Engineering Physics, Mianyang, ChinaCenter for System Reliability and Safety, University of Electronic Science and Technology of China, Chengdu, ChinaReliability is critical for complex engineering systems. Traditionally, reliability analysis and fault diagnosis of complex engineering systems is based on reliability block diagram and fault tree. These methods are limited either on the flexibility for system characterization or on the capability for quantitative analysis. Recently, the Bayesian network has been introduced in reliability engineering, and it has been demonstrated with great flexibility. In this article, the Bayesian network is investigated for reliability analysis and fault diagnosis of complex engineering systems through two real cases. It includes the case of a high-speed train representing the complex system with standardized components and the case of a critical subsystem of a high-power solid-state laser representing the complex system with highly customized components. In particular, Bayesian networks are constructed to model the reliability of these systems, where transformations of reliability block diagram and fault tree into Bayesian networks are presented. Reliability assessment of the systems is obtained through forward inference of Bayesian network. In addition, fault diagnosis is studied for identifying critical components, major causes, and diagnosis routes by utilizing backward inference of Bayesian network.https://doi.org/10.1177/1687814017728853
collection DOAJ
language English
format Article
sources DOAJ
author Shen Chen
Zhen Qi
Dehuai Chen
Liangfu Guo
Weiwen Peng
spellingShingle Shen Chen
Zhen Qi
Dehuai Chen
Liangfu Guo
Weiwen Peng
Investigation of Bayesian network for reliability analysis and fault diagnosis of complex systems with real case applications
Advances in Mechanical Engineering
author_facet Shen Chen
Zhen Qi
Dehuai Chen
Liangfu Guo
Weiwen Peng
author_sort Shen Chen
title Investigation of Bayesian network for reliability analysis and fault diagnosis of complex systems with real case applications
title_short Investigation of Bayesian network for reliability analysis and fault diagnosis of complex systems with real case applications
title_full Investigation of Bayesian network for reliability analysis and fault diagnosis of complex systems with real case applications
title_fullStr Investigation of Bayesian network for reliability analysis and fault diagnosis of complex systems with real case applications
title_full_unstemmed Investigation of Bayesian network for reliability analysis and fault diagnosis of complex systems with real case applications
title_sort investigation of bayesian network for reliability analysis and fault diagnosis of complex systems with real case applications
publisher SAGE Publishing
series Advances in Mechanical Engineering
issn 1687-8140
publishDate 2017-10-01
description Reliability is critical for complex engineering systems. Traditionally, reliability analysis and fault diagnosis of complex engineering systems is based on reliability block diagram and fault tree. These methods are limited either on the flexibility for system characterization or on the capability for quantitative analysis. Recently, the Bayesian network has been introduced in reliability engineering, and it has been demonstrated with great flexibility. In this article, the Bayesian network is investigated for reliability analysis and fault diagnosis of complex engineering systems through two real cases. It includes the case of a high-speed train representing the complex system with standardized components and the case of a critical subsystem of a high-power solid-state laser representing the complex system with highly customized components. In particular, Bayesian networks are constructed to model the reliability of these systems, where transformations of reliability block diagram and fault tree into Bayesian networks are presented. Reliability assessment of the systems is obtained through forward inference of Bayesian network. In addition, fault diagnosis is studied for identifying critical components, major causes, and diagnosis routes by utilizing backward inference of Bayesian network.
url https://doi.org/10.1177/1687814017728853
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