Updated proportional hazards model for equipment residual life prediction

Purpose – Most current condition‐based maintenance (CBM) systems using proportional hazards model (PHM) assume that enough historical data are available. However, in many practical cases, it is usually costly to collect much historical data prior to real practice (model implementation). This paper aims to focus on the necessity and benefits of updating a PHM with new samples generated in the process of model implementation. Design/methodology/approach – First, an updating scheme is presented and embedded into the entire cycle of PHM establishment, its application/implementation, and its updating. Next, a simulation evaluation is conducted based on a typical degradation model. Findings – By updating a PHM using newly generated samples, the precision and reliability of residual life (RL) prediction can be improved, especially close to system failure. Research limitations/implications – The current version of PHM is typically for non‐repairable systems or those systems only receiving renewal maintenance. Further research should focus on the inclusion of the effect of imperfect maintenance. Practical implications – The updating scheme enables maintenance practitioners to more precisely and reliably predict the RL of an in‐operation system, and enhances further CBM decision making. Originality/value – This paper highlights the necessity and benefits of updating a PHM using emerging new training resources, which has not received enough attention in existing research/practice of a PHM.