Health Assessment for Multi-component Systems Based on Proportional Hazards Model and Dynamic Bayesian Network

Abstract

As a key task of prognostics and health management, the health assessment of systems depends on both age and covariate processes. Cox’s proportional hazards model is an effective tool for system health assessment, capable of capturing both the failure rate and the effect of the covariate process. However, most existing literature assumes the system failure as a whole, which exhibits certain limitations when dealing with the failure interactions among components in complex systems. This paper develops a method that combines a dynamic Bayesian network with a proportional hazards model, where a dynamic Bayesian network is utilized to characterize the failure dependency among components failure, and the joint effect of age and covariate processes on component failure is quantified by the proportional hazards model. The integration of two models facilitates the health assessment of complex multi-component systems. Finally, the effectiveness of the proposed model is showcased through a numerical case study.