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An age- and state-dependent Markov model for degradation processes

Author

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  • Massimiliano Giorgio
  • Maurizio Guida
  • Gianpaolo Pulcini

Abstract

Many technological units are subjected during their operating life to a gradual deterioration process that progressively degrades their characteristics until a failure occurs. Statisticians and engineers have almost always modeled degradation phenomena using independent increments processes, which imply that the degradation growth depends, at most, on the unit age. Only a few models have been proposed in which the degradation growth is assumed to depend on the current unit state. In many cases, however, both the current age and the current state of a unit can affect the degradation process. As such, this article proposes a degradation model in which the transition probabilities between unit states depend on both the current age and the current degradation level. Two applications based on real data sets are analyzed and discussed.

Suggested Citation

  • Massimiliano Giorgio & Maurizio Guida & Gianpaolo Pulcini, 2011. "An age- and state-dependent Markov model for degradation processes," IISE Transactions, Taylor & Francis Journals, vol. 43(9), pages 621-632.
  • Handle: RePEc:taf:uiiexx:v:43:y:2011:i:9:p:621-632
    DOI: 10.1080/0740817X.2010.532855
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    Cited by:

    1. Compare, M. & Martini, F. & Zio, E., 2015. "Genetic algorithms for condition-based maintenance optimization under uncertainty," European Journal of Operational Research, Elsevier, vol. 244(2), pages 611-623.
    2. Wang, Changxi & Elsayed, Elsayed A., 2020. "Stochastic modeling of corrosion growth," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    3. Peng, Hao & van Houtum, Geert-Jan, 2016. "Joint optimization of condition-based maintenance and production lot-sizing," European Journal of Operational Research, Elsevier, vol. 253(1), pages 94-107.
    4. Lin, Yan-Hui & Li, Yan-Fu & Zio, Enrico, 2018. "A comparison between Monte Carlo simulation and finite-volume scheme for reliability assessment of multi-state physics systems," Reliability Engineering and System Safety, Elsevier, vol. 174(C), pages 1-11.
    5. KarabaÄŸ, Oktay & Eruguz, Ayse Sena & Basten, Rob, 2020. "Integrated optimization of maintenance interventions and spare part selection for a partially observable multi-component system," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    6. Zhengxin Zhang & Xiaosheng Si & Changhua Hu & Xiangyu Kong, 2015. "Degradation modeling–based remaining useful life estimation: A review on approaches for systems with heterogeneity," Journal of Risk and Reliability, , vol. 229(4), pages 343-355, August.
    7. Compare, M. & Baraldi, P. & Bani, I. & Zio, E. & Mc Donnell, D., 2017. "Development of a Bayesian multi-state degradation model for up-to-date reliability estimations of working industrial components," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 25-40.
    8. Che, Haiyang & Zeng, Shengkui & Guo, Jianbin, 2019. "Reliability assessment of man-machine systems subject to mutually dependent machine degradation and human errors," Reliability Engineering and System Safety, Elsevier, vol. 190(C), pages 1-1.
    9. Liang, Qingzhu & Yang, Yinghao & Peng, Changhong, 2023. "A reliability model for systems subject to mutually dependent degradation processes and random shocks under dynamic environments," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    10. Wang, Jia & Li, Zhigang & Bai, Guanghan & Zuo, Ming J., 2020. "An improved model for dependent competing risks considering continuous degradation and random shocks," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    11. Gregory Levitin & Maxim Finkelstein, 2017. "A new stress–strength model for systems subject to stochastic shocks," Journal of Risk and Reliability, , vol. 231(2), pages 172-179, April.
    12. Pang, Zhenan & Li, Tianmei & Pei, Hong & Si, Xiaosheng, 2023. "A condition-based prognostic approach for age- and state-dependent partially observable nonlinear degrading system," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    13. Liu, Xingheng & Matias, José & Jäschke, Johannes & Vatn, Jørn, 2022. "Gibbs sampler for noisy Transformed Gamma process: Inference and remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    14. Giorgio, Massimiliano & Pulcini, Gianpaolo, 2018. "A new state-dependent degradation process and related model misidentification problems," European Journal of Operational Research, Elsevier, vol. 267(3), pages 1027-1038.
    15. Lin, X. & Basten, R.J.I. & Kranenburg, A.A. & van Houtum, G.J., 2017. "Condition based spare parts supply," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 240-248.
    16. Zhang, Aibo & Wu, Shengnan & Fan, Dongming & Xie, Min & Cai, Baoping & Liu, Yiliu, 2022. "Adaptive testing policy for multi-state systems with application to the degrading final elements in safety-instrumented systems," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    17. Guida, M. & Pulcini, G., 2013. "The inverse Gamma process: A family of continuous stochastic models for describing state-dependent deterioration phenomena," Reliability Engineering and System Safety, Elsevier, vol. 120(C), pages 72-79.
    18. Wang, Jia & Bai, Guanghan & Li, Zhigang & Zuo, Ming J., 2020. "A general discrete degradation model with fatal shocks and age- and state-dependent nonfatal shocks," Reliability Engineering and System Safety, Elsevier, vol. 193(C).

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