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Multistate degradation and supervised estimation methods for a condition-monitored device

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  • Ramin Moghaddass
  • Ming Zuo

Abstract

Multistate reliability has received significant attention over the past decades, particularly its application to mechanical devices that degrade over time. This degradation can be represented by a multistate continuous-time stochastic process. This article considers a device with discrete multistate degradation, which is monitored by a condition monitoring indicator through an observation process. A general stochastic process called the nonhomogeneous continuous-time hidden semi-Markov process is employed to model the degradation and observation processes associated with this type of device. Then, supervised parametric and nonparametric estimation methods are developed to estimate the maximum likelihood estimators of the main characteristics of the model. Finally, the correctness and empirical consistency of the estimators are evaluated using a simulation-based numerical experiment.

Suggested Citation

  • Ramin Moghaddass & Ming Zuo, 2014. "Multistate degradation and supervised estimation methods for a condition-monitored device," IISE Transactions, Taylor & Francis Journals, vol. 46(2), pages 131-148.
    • Handle: RePEc:taf:uiiexx:v:46:y:2014:i:2:p:131-148
    DOI: 10.1080/0740817X.2013.770188
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    Cited by:

    1. Wu, Jianing & Yan, Shaoze & Zuo, Ming J., 2016. "Evaluating the reliability of multi-body mechanisms: A method considering the uncertainties of dynamic performance," Reliability Engineering and System Safety, Elsevier, vol. 149(C), pages 96-106.
    2. Gao, Hongda & Cui, Lirong & Dong, Qinglai, 2020. "Reliability modeling for a two-phase degradation system with a change point based on a Wiener process," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    3. Liu, Jie & Zio, Enrico, 2017. "Weighted-feature and cost-sensitive regression model for component continuous degradation assessment," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 210-217.
    4. D’Amico, Guglielmo & Petroni, Filippo, 2023. "ROCOF of higher order for semi-Markov processes," Applied Mathematics and Computation, Elsevier, vol. 441(C).
    5. Eleftheroglou, Nick & Zarouchas, Dimitrios & Loutas, Theodoros & Alderliesten, Rene & Benedictus, Rinze, 2018. "Structural health monitoring data fusion for in-situ life prognosis of composite structures," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 40-54.
    6. 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.
    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).

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