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
Hlavní autori: Zheng, Xiaohu, Yao, Wen, Xu, Yingchun, Chen, Xiaoqian
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Barking Elsevier Ltd 01.10.2020
Elsevier BV
Predmet:
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
Complex multistate independent systems
compression algorithm
reliability analysis
Bayesian network
ISSN:0951-8320, 1879-0836
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Popis
Shrnutí:•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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ISSN:0951-8320
1879-0836
DOI:10.1016/j.ress.2020.107011