Mixed-Effects Nonhomogeneous Poisson Process Model for Multiple Repairable Systems

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초록

The nonhomogeneous Poisson process (NHPP) has become a useful approach for modeling failure patterns of recurrent failure data revealed by minimal repairs from an individual repairable system. Sometimes, multiple repairable systems may present system-to-system variability owing to operation environments or working intensities of individual systems. In this paper, we go over the application of generalized mixed-effects models to recurrent failure data from multiple repairable systems based on the NHPP. The generalized mixed-effects models explicitly involve between-system variation through randomeffects, along with a common baseline for all the systems through fixed-effects for non-normal data. Details on estimation of the parameters of the mixed-effects NHPP models and construction of their confidence intervals are examined. An applicative example shows prominent proof of the mixed-effects NHPP models for the purpose of reliability analysis.

키워드

Data modelsAnalytical modelsReliabilityMaintenance engineeringAtmospheric modelingNumerical modelsBayes methodsEmpirical Bayesminimal repairpower law processrandom-effects modelreliability analysisRELIABILITY-ANALYSISREGRESSIONPOWER
제목
Mixed-Effects Nonhomogeneous Poisson Process Model for Multiple Repairable Systems
저자
Mun, Byeong MinKvam, Paul H.Bae, Suk Joo
DOI
10.1109/ACCESS.2021.3077605
발행일
2021-05
유형
Article
저널명
IEEE Access
9
페이지
71900 ~ 71908

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