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Reliability analysis of complex multi-state system based on universal generating function and Bayesian Network

Author

Listed:
  • Xu Liu
  • Wen Yao
  • Xiaohu Zheng
  • Yingchun Xu
  • Xiaoqian Chen

Abstract

In the complex multi-state system (MSS), reliability analysis is an important research content, both for equipment design, manufacturing, operation and maintenance. Universal Generating Function (UGF) is an essential method in reliability analysis, which efficiently obtains system reliability by a fast algebraic procedure. However, when structural relationships between subsystems or components are unclear or without explicit expressions, the UGF method is difficult to use or not applicable at all. Bayesian Network (BN) has a natural advantage in terms of reliability inference for the relationship without explicit expressions. When the number of components is extremely large, though, it has the defects of low efficiency. To overcome the respective shortcomings of UGF and BN, a novel reliability analysis method called UGF-BN is proposed for the complex MSS. In the UGF-BN framework, the UGF method is first used to analyze the bottom components with a large number. Then probability distributions obtained are taken as the input of BN. Finally, the reliability of the complex MSS is modeled by the BN method. This proposed method improves the computational efficiency, especially for the MSS with a large number of bottom components. Besides, the aircraft reliability-based design optimization based on the UGF-BN method is further studied with budget constraints on mass, power, and cost. Finally, two cases are used to demonstrate and verify the proposed method.

Suggested Citation

  • Xu Liu & Wen Yao & Xiaohu Zheng & Yingchun Xu & Xiaoqian Chen, 2024. "Reliability analysis of complex multi-state system based on universal generating function and Bayesian Network," Journal of Risk and Reliability, , vol. 238(4), pages 797-811, August.
    • Handle: RePEc:sae:risrel:v:238:y:2024:i:4:p:797-811
    DOI: 10.1177/1748006X231173301
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    References listed on IDEAS

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