Optimal Inspection and Replacement Policy Based on Experimental Degradation Data with Covariates

Xiujie Zhao (1) , Olivier Gaudoin (2) , Laurent Doyen (2) , Min Xie (1)

1 SEEM - Department of Systems Engineering and Engineering Management
2 FIGAL - Fiabilité et Géométrie Aléatoire

In this paper, a novel maintenance model is proposed for single-unit systems with atypical degradation path of which the pattern is influenced by inspections. After each inspection, the system degradation is assumed to decrease by a random value instantaneously. Meanwhile, the degrading rate is elevated due to the inspection. Considering the double effects of inspections, we develop a parameter estimation procedure for such systems from experimental data obtained via accelerated degradation tests with environmental covariates. Next, the inspection and replacement policy is optimized with the objective to minimize the expected long-run cost rate (ELRCR). Inspections are assumed to be non-periodically scheduled. A numerical algorithm that combines analytical and simulation methods is presented to evaluate the ELRCR. Afterward, we investigate the robustness of maintenance policies for such systems by taking the parameter uncertainty into account with the aid of large-sample approximation and parametric bootstrapping. The application of the proposed method is illustrated by degradation data from electric industry.

Mots clés: condition-based maintenance; degradation modeling; imperfect maintenance; large-sample approximation; maximum likelihood estimation

Domaines: Statistiques [math.ST]

Xiujie Zhao, Olivier Gaudoin, Laurent Doyen, Min Xie. Optimal Inspection and Replacement Policy Based on Experimental Degradation Data with Covariates. IISE Transactions, 2019, 51 (3), pp.322-336. ⟨10.1080/24725854.2018.1488308⟩. ⟨hal-01883037⟩