Taylor & Francis Group
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Statistical Modeling of the Effectiveness of Preventive Maintenance for Repairable Systems

Version 2 2023-09-19, 01:41
Version 1 2023-07-26, 19:20
posted on 2023-09-19, 01:41 authored by Xin Ye, Jiaxiang Cai, Loon Ching Tang, Zhi-Sheng Ye

Preventive maintenance (PM) is commonly adopted in practice to improve a system’s health condition and reduce the risk of unexpected failures. When a PM action is poorly performed, however, it is likely to have adverse effects on system reliability. We observe this phenomenon when evaluating the effectiveness of a PM program for a fleet of service vehicles based on their four-year operating data. This phenomenon is also commonly reported in the maintenance of vehicles and aircraft. Motivated by this observation, we propose a statistical model for repairable systems by taking potential PM adverse effects into account. In the formulation, the baseline failure process without PM effects is modeled by a nonhomogeneous Poisson process. When a PM action is performed, its effect on the failure process is modeled as a multiplicative random effect on the system rate of occurrence of failures. Statistical inference under the proposed model is discussed, and we further develop goodness-of-fit test procedures to validate the adequacy of this model. The above-mentioned service vehicle operating data are used to demonstrate the proposed methods.


This work was supported by the Future Resilient Systems project at the Singapore-ETH Centre (SEC) established by Singapore’s National Research Foundation under its CREATE programme.