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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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 |
work_keys_str_mv |
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