Work

Condition-Based Maintenance Decision-Making for Multi-Component Systems with Integrated Dynamic Bayesian Network and Proportional Hazards Model

Year: 2025

Type: article

Abstract: A condition-based maintenance decision-making framework for multi-component systems is proposed in this work by integrating dynamic Bayesian network (DBN) with proportional hazards model (PHM). The framework is designed to address the challenge of handling mixed failure types and complex failure dependencies, which often lead to inaccurate maintenance decisions in existing methods. In this integrated model, the DBN captures the failure evolution and both dynamic and static dependencies among components, while the PHM enhances the capability to characterize mixed failure interactions, thereby enabling the coverage of three common types of failure dependencies in multi-component systems. The model is formulated and solved using a finite-horizon Markov decision process (MDP), with the optimal maintenance strategy obtained by maximizing the total expected reward. Numerical case studies demonstrate the framework’s flexibility in handling mixed failures and complex dependencies, showing its potential to effectively support condition-based maintenance decision-making for complex multi-component systems.

Source: Applied Sciences

DOI: 10.3390/app152312793

Authors: Shizheng Li, Canjiong Yao, Pengfei Xu, Jinyan Guo, Guoqing Wang, Jing Tang

Institutions: Ministry of Industry and Information Technology, Jilin University

Language: English

Topic: Reliability and Maintenance Optimization

Subfield: Safety, Risk, Reliability and Quality

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funders: Natural Science Foundation of Chongqing, Basic and Applied Basic Research Foundation of Guangdong Province