Open Access

ARTICLE

System Reliability Analysis Method Based on T-S FTA and HE-BN

Qing Xia¹, Yonghua Li^2,*, Dongxu Zhang², Yufeng Wang²

1 School of Mechanical Engineering, Dalian Jiaotong University, Dalian, 116028, China
2 School of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian, 116028, China

* Corresponding Author: Yonghua Li. Email:

(This article belongs to the Special Issue: Computer-Aided Uncertainty Modeling and Reliability Evaluation for Complex Engineering Structures)

Computer Modeling in Engineering & Sciences 2024, 138(2), 1769-1794. https://doi.org/10.32604/cmes.2023.030724

Received 20 April 2023; Accepted 22 May 2023; Issue published 17 November 2023

Abstract

For high-reliability systems in military, aerospace, and railway fields, the challenges of reliability analysis lie in dealing with unclear failure mechanisms, complex fault relationships, lack of fault data, and uncertainty of fault states. To overcome these problems, this paper proposes a reliability analysis method based on T-S fault tree analysis (T-S FTA) and Hyper-ellipsoidal Bayesian network (HE-BN). The method describes the connection between the various system fault events by T-S fuzzy gates and translates them into a Bayesian network (BN) model. Combining the advantages of T-S fault tree modeling with the advantages of Bayesian network computation, a reliability modeling method is proposed that can fully reflect the fault characteristics of complex systems. Experts describe the degree of failure of the event in the form of interval numbers. The knowledge and experience of experts are fused with the D-S evidence theory to obtain the initial failure probability interval of the BN root node. Then, the Hyper-ellipsoidal model (HM) constrains the initial failure probability interval and constructs a HE-BN for the system. A reliability analysis method is proposed to solve the problem of insufficient failure data and uncertainty in the degree of failure. The failure probability of the system is further calculated and the key components that affect the system’s reliability are identified. The proposed method accounts for the uncertainty and incompleteness of the failure data in complex multi-state systems and establishes an easily computable reliability model that fully reflects the characteristics of complex faults and accurately identifies system weaknesses. The feasibility and accuracy of the method are further verified by conducting case studies.

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