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A parameter estimation method for a condition-monitored device under multi-state deterioration

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  • Moghaddass, Ramin
  • Zuo, Ming J.

Abstract

The overall performance of a mechanical device under random shocks, fatigue, and gradual degradation may continuously deteriorate over time, leading to multi-state health conditions. This deterioration can be represented by a continuous-time degradation process – with multiple discrete states – that reflects the relative degree of deterioration. This paper focuses on a condition-monitored device with multi-state deterioration, where its degradation state is not directly observable and only incomplete information is available through condition monitoring. After modeling this multi-state device, an unsupervised parameter estimation method is developed, which employs historical condition monitoring information to estimate the unknown characteristic parameters of the degradation process and the observation process. The results are evaluated through numerical experiments.

Suggested Citation

  • Moghaddass, Ramin & Zuo, Ming J., 2012. "A parameter estimation method for a condition-monitored device under multi-state deterioration," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 94-103.
    • Handle: RePEc:eee:reensy:v:106:y:2012:i:c:p:94-103
    DOI: 10.1016/j.ress.2012.05.004
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    Cited by:

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    2. Shijia Du & Lirong Cui & Cong Lin, 2016. "Some reliability indexes and sojourn time distributions for a repairable degradation model," Journal of Risk and Reliability, , vol. 230(3), pages 334-349, June.
    3. Jiang, Tao & Liu, Yu, 2017. "Parameter inference for non-repairable multi-state system reliability models by multi-level observation sequences," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 3-15.
    4. Chen, Gaige & Chen, Jinglong & Zi, Yanyang & Miao, Huihui, 2017. "Hyper-parameter optimization based nonlinear multistate deterioration modeling for deterioration level assessment and remaining useful life prognostics," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 517-526.
    5. de Jonge, Bram & Teunter, Ruud & Tinga, Tiedo, 2017. "The influence of practical factors on the benefits of condition-based maintenance over time-based maintenance," Reliability Engineering and System Safety, Elsevier, vol. 158(C), pages 21-30.
    6. Moghaddass, Ramin & Zuo, Ming J., 2014. "An integrated framework for online diagnostic and prognostic health monitoring using a multistate deterioration process," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 92-104.
    7. Zhao, Yunfei & Gao, Wei & Smidts, Carol, 2021. "Sequential Bayesian inference of transition rates in the hidden Markov model for multi-state system degradation," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    8. Sameer Al-Dahidi & Francesco Di Maio & Piero Baraldi & Enrico Zio, 2017. "A locally adaptive ensemble approach for data-driven prognostics of heterogeneous fleets," Journal of Risk and Reliability, , vol. 231(4), pages 350-363, August.
    9. Liu, Yu & Liu, Qinzhen & Xie, Chaoyang & Wei, Fayuan, 2019. "Reliability assessment for multi-state systems with state transition dependency," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 276-288.

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