Publication Type

Journal Article

Version

acceptedVersion

Publication Date

11-2018

Abstract

Failure of a repairable system may be attributed to operators’ misuse or system deterioration. The misuse may further deteriorate the system under normal operating conditions. Motivated by a real-world data set that records the recurrence times of misuse-induced failures and the normal-operation failures, this study proposes a stochastic process model for recurrence data analysis, where one type of failures is affected by the other. A non-homogeneous Poisson process and a trend-renewal process are separately used as the baseline event process models for the misuse-induced failures and the normal-operation failures, respectively. These two models are then combined by treating the event count of misuse-induced failures as covariate of the event process of normal-operation failures. A Bayesian framework is developed for parameter estimation and dependence tests of the two failure modes. A simulation study and the recurrence data from a manufacturing system are used to demonstrate the proposed method.

Keywords

Bayesian reliability, Repairable system, Bivariate point process, Non-homogeneous Poisson process, Recurrence data

Discipline

Operations Research, Systems Engineering and Industrial Engineering | Risk Analysis

Research Areas

Integrative Research Areas

Publication

Reliability Engineering & System Safety

Volume

171

First Page

87

Last Page

98

ISSN

0951-8320

Identifier

10.1016/j.ress.2017.11.016

Publisher

Elsevier

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1016/j.ress.2017.11.016

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