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

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.