STOCHASTIC MODELING OF IMPERFECT REPAIR BASED ON GRP-WEIBULL APPLIED TO WEAPONS SYSTEMS
Abstract
Reliability modeling in repairable systems frequently relies on binary assumptions of perfect or minimal repair, creating an analytical gap by overlooking continuous degradation in critical assets under severe wear. Given the risk of mission failure and the imperative to ensure operational readiness, this study aimed to develop an imperfect repair stochastic model based on the Generalized Renewal Process integrated with stochastic degradation modeling (GRP–Weibull). The approach applies Kijima’s virtual age theory (Type II) to evaluate the impact of doctrine-defined maintenance echelons on the conditional reliability and operational availability of defense systems. The methodology comprised Maximum Likelihood Estimation (MLE) of failure data implemented in R software, followed by Monte Carlo Simulation to project system behavior after preventive interventions. The results demonstrated that the model accurately quantifies asset rejuvenation via the parameter (ρ), enabling the determination of the optimal maintenance periodicity before reliability reaches critical thresholds. This research addresses methodological gaps in classical reliability engineering, establishing a robust predictive logistics tool to sustain operational availability.