Optimal inspection and replacement policy based on experimental degradation data with covariates

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

34 Scopus Citations
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Original languageEnglish
Pages (from-to)322-336
Journal / PublicationIISE Transactions
Issue number3
Online published28 Jun 2018
Publication statusPublished - 2019


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.

Research Area(s)

  • condition-based maintenance, degradation modeling, imperfect maintenance, maximum likelihood estimation, large-sample approximation