Reliability Analysis of Complex Multi-State System Based on Universal Generating Function and Bayesian Network

TL;DR

This paper introduces a novel UGF-BN method combining Universal Generating Function and Bayesian Network techniques to efficiently analyze the reliability of complex multi-state systems, especially with large component numbers.

Contribution

The paper proposes a new hybrid reliability analysis method that overcomes the limitations of UGF and BN individually, improving efficiency for large-scale systems.

Findings

Enhanced computational efficiency for large MSS

Effective modeling of complex component relationships

Validated with two case studies

Abstract

In the complex multi-state system (MSS), reliability analysis is a significant research content, both for equipment design, manufacturing, usage and maintenance. Universal Generating Function (UGF) is an important method in the reliability analysis, which efficiently obtains the system reliability by a fast algebraic procedure. However, when structural relationships between subsystems or components are not clear 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 uncertainty inference for the relationship without explicit expressions. For the number of components is extremely large, though, it has the defects of low efficiency. To overcome the respective defects 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 firstly 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 the 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.