Evaluating the Performance and Robustness of PIR and QIR Maintenance strategies Using Monte Carlo Method

Abstract

Abstract In the literature, maintenance plan efficacy is often assessed based on the long-term predicted maintenance cost rate, indicating a performance-centric approach. However, this criteria doesn't account for the fluctuation in maintenance costs over renewal cycles, and typical solutions may not be adequate from a risk management standpoint, a robustness viewpoint. This study tries to rethink standard solutions considering both performance and robustness, and thus, offer more suitable maintenance options.Specifically, using the long-term expected maintenance cost rate as the performance metric and the variance of maintenance cost per renewal cycle as the robustness metric, the study examines two representatives of time-based and condition-based maintenance approaches: a block replacement strategy and a periodic inspection and replacement strategy. Mathematical cost models are created based on the homogeneous Gamma degradation process and probability theory.Comparative study of both maintenance techniques demonstrates that the higher-performing approach carries a larger amount of risk. Consequently, a full examination of both performance and resilience is required in selecting a more dependent maintenance option. These maintenance solutions, together with the employment of the Monte Carlo Method, are contrasted against each other using a unique criteria that analyzes the degree of performance and robustness of each adaptation in maintenance decision-making.