Statistical inference for imperfect maintenance models with missing data

Yann Dijoux, Mitra Fouladirad and Dinh Tuan Nguyen

Reliability Engineering and System Safety, 2016, vol. 154, issue C, 84-96

Abstract: The paper considers complex industrial systems with incomplete maintenance history. A corrective maintenance is performed after the occurrence of a failure and its efficiency is assumed to be imperfect. In maintenance analysis, the databases are not necessarily complete. Specifically, the observations are assumed to be window-censored. This situation arises relatively frequently after the purchase of a second-hand unit or in the absence of maintenance record during the burn-in phase. The joint assessment of the wear-out of the system and the maintenance efficiency is investigated under missing data. A review along with extensions of statistical inference procedures from an observation window are proposed in the case of perfect and minimal repair using the renewal and Poisson theories, respectively. Virtual age models are employed to model imperfect repair. In this framework, new estimation procedures are developed. In particular, maximum likelihood estimation methods are derived for the most classical virtual age models. The benefits of the new estimation procedures are highlighted by numerical simulations and an application to a real data set.

Keywords: Statistical inference; Maximum likelihood estimation; Reliability analysis; Repairable system; Imperfect maintenance; Virtual age models; Missing data; Window censoring; Renewal theory (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:154:y:2016:i:c:p:84-96

DOI: 10.1016/j.ress.2016.05.017

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