An Advanced Framework for Predictive Maintenance Decisions: Integrating the Proportional Hazards Model and Machine Learning Techniques under CBM Multi-Covariate Scenarios

Abstract

Under Condition-Based Maintenance, the Proportional Hazards Model (PHM) uses Cox’s partial regression and vital signs as covariates to estimate risk for predictive management. However, maintenance faces challenges when dealing with a multi-covariate scenario due to the impact of the conditions’ heterogeneity on the intervention decisions, especially when the combined measurement lacks a physical interpretation. Therefore, we propose an advanced framework based on a PHM-machine learning formulation integrating four key areas: covariate prioritization, covariate weight estimation, state band definition, and the generation of an enhanced predictive intervention policy. The paper validates the framework’s effectiveness through a comparative analysis of reliability metrics in a case study using real condition monitoring data from an energy company. While the traditional log-likelihood minimization may fall short in covariate weight estimation, sensitivity analyses reveal that the proposed policy using IPOPT and a non-scaler transformation results in consistent prediction quality. Given the challenge of interpreting merged covariates, the scheme yields improved results compared to expert criteria. Finally, the advanced framework strengthens the PHM modeling by coherently integrating diverse covariate scenarios for predictive maintenance purposes.

Keywords

• proportional hazards model
• condition-based maintenance
• multi-covariate scenarios
• machine learning
• conditional reliability function
• remaining useful life