Algorithms for Bayesian network modeling and reliability inference of complex multistate systems: Part I – Independent systems
•Multistate compression and inference algorithms are applicable to any complex systems.•Given the evidence, backward inference algorithm can update the probability distributions of all nodes.•The potential application of the proposed algorithms in the reliability-based optimization for complex engin...
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| Vydané v: | Reliability engineering & system safety Ročník 202; s. 107011 |
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| Médium: | Journal Article |
| Jazyk: | English |
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01.10.2020
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| ISSN: | 0951-8320, 1879-0836 |
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| Abstract | •Multistate compression and inference algorithms are applicable to any complex systems.•Given the evidence, backward inference algorithm can update the probability distributions of all nodes.•The potential application of the proposed algorithms in the reliability-based optimization for complex engineering systems.
As the number of complex multistate systems’ components increases, one major challenge to analyze the reliabilities of complex multistate systems by Bayesian network (BN) is that the memory storage requirements (MSRs) of conditional probability table (CPT) increase exponentially. When the components reach a certain amount, the MSRs of CPT will exceed the computer's random access memory (RAM). To solve this problem, this two-part paper proposes a novel multistate compression algorithm to compress the CPT so that the MSRs of CPT can be reduced apparently. In this Part I, an independent multistate inference algorithm is proposed to perform the inference of BN based on the compressed CPT for the complex multistate independent systems. Given the evidence of system, the backward inference algorithm is proposed to update the probability distributions of compoents. The above proposed algorithms can be generally applied to any complex multistate independent system without constraints on system structure and state configurations. In addition, the Part II studies the application of compression idea in the complex multistate dependent systems. Finally, two case studies are used to validate the performance of the proposed algorithms. |
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| AbstractList | •Multistate compression and inference algorithms are applicable to any complex systems.•Given the evidence, backward inference algorithm can update the probability distributions of all nodes.•The potential application of the proposed algorithms in the reliability-based optimization for complex engineering systems.
As the number of complex multistate systems’ components increases, one major challenge to analyze the reliabilities of complex multistate systems by Bayesian network (BN) is that the memory storage requirements (MSRs) of conditional probability table (CPT) increase exponentially. When the components reach a certain amount, the MSRs of CPT will exceed the computer's random access memory (RAM). To solve this problem, this two-part paper proposes a novel multistate compression algorithm to compress the CPT so that the MSRs of CPT can be reduced apparently. In this Part I, an independent multistate inference algorithm is proposed to perform the inference of BN based on the compressed CPT for the complex multistate independent systems. Given the evidence of system, the backward inference algorithm is proposed to update the probability distributions of compoents. The above proposed algorithms can be generally applied to any complex multistate independent system without constraints on system structure and state configurations. In addition, the Part II studies the application of compression idea in the complex multistate dependent systems. Finally, two case studies are used to validate the performance of the proposed algorithms. As the number of complex multistate systems' components increases, one major challenge to analyze the reliabilities of complex multistate systems by Bayesian network (BN) is that the memory storage requirements (MSRs) of conditional probability table (CPT) increase exponentially. When the components reach a certain amount, the MSRs of CPT will exceed the computer's random access memory (RAM). To solve this problem, this two-part paper proposes a novel multistate compression algorithm to compress the CPT so that the MSRs of CPT can be reduced apparently. In this Part I, an independent multistate inference algorithm is proposed to perform the inference of BN based on the compressed CPT for the complex multistate independent systems. Given the evidence of system, the backward inference algorithm is proposed to update the probability distributions of compoents. The above proposed algorithms can be generally applied to any complex multistate independent system without constraints on system structure and state configurations. In addition, the Part II studies the application of compression idea in the complex multistate dependent systems. Finally, two case studies are used to validate the performance of the proposed algorithms. |
| ArticleNumber | 107011 |
| Author | Zheng, Xiaohu Xu, Yingchun Chen, Xiaoqian Yao, Wen |
| Author_xml | – sequence: 1 givenname: Xiaohu surname: Zheng fullname: Zheng, Xiaohu organization: College of Aerospace Science and Engineering, National University of Defense Technology, No. 109 Deya Road, Kaifu District, Changsha, Hunan Province, China 410073 – sequence: 2 givenname: Wen surname: Yao fullname: Yao, Wen email: wendy0782@126.com organization: National Innovation Institute of Defense Technology, Chinese Academy of Military Science, No.53, East Main Street, Fengtai District, Beijing, China 100071 – sequence: 3 givenname: Yingchun surname: Xu fullname: Xu, Yingchun organization: College of Aerospace Science and Engineering, National University of Defense Technology, No. 109 Deya Road, Kaifu District, Changsha, Hunan Province, China 410073 – sequence: 4 givenname: Xiaoqian surname: Chen fullname: Chen, Xiaoqian organization: National Innovation Institute of Defense Technology, Chinese Academy of Military Science, No.53, East Main Street, Fengtai District, Beijing, China 100071 |
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| Cites_doi | 10.1016/S0167-4730(01)00017-0 10.1016/S0951-8320(03)00121-2 10.1016/j.ress.2019.04.011 10.1016/j.ress.2013.02.014 10.1109/TIT.1977.1055714 10.1016/j.jneumeth.2011.10.025 10.1016/j.ress.2017.05.003 10.1061/(ASCE)CP.1943-5487.0000699 10.1016/j.ress.2005.11.037 10.1016/j.ress.2013.12.001 10.1016/0004-3702(86)90072-X 10.1016/j.paerosci.2011.05.001 10.1061/(ASCE)IS.1943-555X.0000384 10.1109/TR.2015.2419620 10.1016/j.ress.2016.07.022 10.1007/BFb0055097 |
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| Keywords | Complex multistate independent systems compression algorithm reliability analysis Bayesian network |
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| Snippet | •Multistate compression and inference algorithms are applicable to any complex systems.•Given the evidence, backward inference algorithm can update the... As the number of complex multistate systems' components increases, one major challenge to analyze the reliabilities of complex multistate systems by Bayesian... |
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| SubjectTerms | Algorithms Bayesian analysis Bayesian network Complex multistate independent systems Component reliability Compression compression algorithm Conditional probability Inference Network reliability Random access memory reliability analysis Reliability engineering Storage requirements |
| Title | Algorithms for Bayesian network modeling and reliability inference of complex multistate systems: Part I – Independent systems |
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