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Industrial equipment reliability estimation: A Bayesian Weibull regression model with covariate selection

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  • Compare, M.
  • Baraldi, P.
  • Bani, I.
  • Zio, E.
  • McDonnell, D.

Abstract

A three-state continuous-time semi-Markov process is used to model the degradation of an industrial equipment. The transition times are assumed Weibull-distributed and influenced by a set of covariates. A Weibull Regression Model is developed within the Bayesian probability framework, to account for the influence of these covariates and estimate the model parameters with the related uncertainty, on the basis of few data and expert judgment. The number of covariates is reduced by a two-step selection procedure derived from the condition monitoring engineering practice. The developed model enables estimating reliability and time-dependent state probabilities for a component degrading in given operational and ambient conditions, represented by a vector of covariates. The model is illustrated by way of a real case study concerning the degradation process affecting diaphragm valves used in the biopharmaceutical industry.

Suggested Citation

  • Compare, M. & Baraldi, P. & Bani, I. & Zio, E. & McDonnell, D., 2020. "Industrial equipment reliability estimation: A Bayesian Weibull regression model with covariate selection," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    • Handle: RePEc:eee:reensy:v:200:y:2020:i:c:s0951832019300481
    DOI: 10.1016/j.ress.2020.106891
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    References listed on IDEAS

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    Cited by:

    1. Zhou, Hang & Lopes Genez, Thiago Augusto & Brintrup, Alexandra & Parlikad, Ajith Kumar, 2022. "A hybrid-learning decomposition algorithm for competing risk identification within fleets of complex engineering systems," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    2. Chang, Ping-Chen, 2022. "MC-based simulation approach for two-terminal multi-state network reliability evaluation without knowing d-MCs," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    3. Hong Xu & Baorui Zhang, 2022. "Diverse and Flexible Coping Strategy for Nuclear Safety: Opportunities and Challenges," Energies, MDPI, vol. 15(17), pages 1-21, August.
    4. Chang, Ping-Chen & Huang, Ding-Hsiang & Lin, Yi-Kuei & Nguyen, Thi-Phuong, 2021. "Reliability and maintenance models for a time-related multi-state flow network via d-MC approach," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    5. Wu, Shaomin, 2021. "Two methods to approximate the superposition of imperfect failure processes," Reliability Engineering and System Safety, Elsevier, vol. 207(C).

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