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Repairable system analysis in presence of covariates and random effects

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  • Giorgio, M.
  • Guida, M.
  • Pulcini, G.

Abstract

This paper aims to model the failure pattern of repairable systems in presence of explained and unexplained heterogeneity. The failure pattern of each system is described by a Power Law Process. Part of the heterogeneity among the patterns is explained through the use of a covariate, and the residual unexplained heterogeneity (random effects) is modeled via a joint probability distribution on the PLP parameters. The proposed approach is applied to a real set of failure time data of powertrain systems mounted on 33 buses employed in urban and suburban routes. Moreover, the joint probability distribution on the PLP parameters estimated from the data is used as an informative prior to make Bayesian inference on the future failure process of a generic system belonging to the same population and employed in an urban or suburban route under randomly chosen working conditions.

Suggested Citation

  • Giorgio, M. & Guida, M. & Pulcini, G., 2014. "Repairable system analysis in presence of covariates and random effects," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 271-281.
    • Handle: RePEc:eee:reensy:v:131:y:2014:i:c:p:271-281
    DOI: 10.1016/j.ress.2014.04.009
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    References listed on IDEAS

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    1. Majeske, Karl D., 2007. "A non-homogeneous Poisson process predictive model for automobile warranty claims," Reliability Engineering and System Safety, Elsevier, vol. 92(2), pages 243-251.
    2. Taghipour, Sharareh & Banjevic, Dragan, 2011. "Trend analysis of the power law process using Expectation–Maximization algorithm for data censored by inspection intervals," Reliability Engineering and System Safety, Elsevier, vol. 96(10), pages 1340-1348.
    3. Finkelstein, Maxim, 2007. "Imperfect repair and lifesaving in heterogeneous populations," Reliability Engineering and System Safety, Elsevier, vol. 92(12), pages 1671-1676.
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    Citations

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    Cited by:

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    2. Rezgar Zaki & Abbas Barabadi & Javad Barabady & Ali Nouri Qarahasanlou, 2022. "Observed and unobserved heterogeneity in failure data analysis," Journal of Risk and Reliability, , vol. 236(1), pages 194-207, February.
    3. Ali Nouri Qarahasanlou & Ali Zamani & Abbas Barabadi & Mahdi Mokhberdoran, 2021. "Resilience Assessment: A Performance-Based Importance Measure," Energies, MDPI, vol. 14(22), pages 1-16, November.
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    5. Brenière, Léa & Doyen, Laurent & Bérenguer, Christophe, 2020. "Virtual age models with time-dependent covariates: A framework for simulation, parametric inference and quality of estimation," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    6. Awat Ghomghaleh & Reza Khaloukakaie & Mohammad Ataei & Abbas Barabadi & Ali Nouri Qarahasanlou & Omeid Rahmani & Amin Beiranvand Pour, 2020. "Prediction of remaining useful life (RUL) of Komatsu excavator under reliability analysis in the Weibull-frailty model," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-16, July.
    7. Garmabaki, A.H.S. & Ahmadi, Alireza & Block, Jan & Pham, Hoang & Kumar, Uday, 2016. "A reliability decision framework for multiple repairable units," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 78-88.
    8. Slimacek, Vaclav & Lindqvist, Bo Henry, 2017. "Nonhomogeneous Poisson process with nonparametric frailty and covariates," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 75-83.
    9. Yizhen, Peng & Yu, Wang & Jingsong, Xie & Yanyang, Zi, 2020. "Adaptive stochastic-filter-based failure prediction model for complex repairable systems under uncertainty conditions," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    10. Slimacek, Vaclav & Lindqvist, Bo Henry, 2016. "Nonhomogeneous Poisson process with nonparametric frailty," Reliability Engineering and System Safety, Elsevier, vol. 149(C), pages 14-23.

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