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A degradation modeling and reliability estimation method based on Wiener process and evidential variable

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  • Liu, Di
  • Wang, Shaoping

Abstract

Based on Wiener process and evidential variable, a reliability estimation and degradation modeling method is proposed in this paper. Wiener process is combined with evidence theory by applying evidential variable to describe model parameters. The basic probability assignments of evidential model parameters are evaluated based on likelihoods. Bayesian inference method is used to fuse degradation data and update evidential model parameters. Based on a wildly used actual degradation dataset, the distinguish features of the proposed method are verified. Due to powerful uncertainty describing ability of evidential variable, the proposed method shows superiority on online prediction and population evaluation compared to random variable based method, such as improvements in stability and accuracy. Furthermore, the proposed method can be used to evaluate population reliability without increasing parameter size, resulting in stochastic process become more practical under small sample conditions.

Suggested Citation

  • Liu, Di & Wang, Shaoping, 2020. "A degradation modeling and reliability estimation method based on Wiener process and evidential variable," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    • Handle: RePEc:eee:reensy:v:202:y:2020:i:c:s0951832019315418
    DOI: 10.1016/j.ress.2020.106957
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    References listed on IDEAS

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    7. Liu, Di & Wang, Shaoping & Zhang, Chao & Tomovic, Mileta, 2018. "Bayesian model averaging based reliability analysis method for monotonic degradation dataset based on inverse Gaussian process and Gamma process," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 25-38.
    8. Li Sun & Xiaohui Gu & Pu Song, 2016. "Accelerated Degradation Process Analysis Based on the Nonlinear Wiener Process with Covariates and Random Effects," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-13, December.
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    Cited by:

    1. Xu, Yanwen & Kohtz, Sara & Boakye, Jessica & Gardoni, Paolo & Wang, Pingfeng, 2023. "Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
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    3. Liu, Di & Wang, Shaoping & Zhang, Chao, 2022. "Reliability estimation from two types of accelerated testing data based on an artificial neural network supported Wiener process," Applied Mathematics and Computation, Elsevier, vol. 417(C).
    4. Xinlong Li & Yan Ran & Fangming Wan & Hui Yu & Genbao Zhang & Yan He, 2022. "Condition-based maintenance strategy optimization of meta-action unit considering imperfect preventive maintenance based on Wiener process," Flexible Services and Manufacturing Journal, Springer, vol. 34(1), pages 204-233, March.
    5. Liu, Di & Wang, Shaoping, 2021. "Reliability estimation from lifetime testing data and degradation testing data with measurement error based on evidential variable and Wiener process," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    6. Liu, Di & Wang, Shaoping & Cui, Xiaoyu, 2022. "An artificial neural network supported Wiener process based reliability estimation method considering individual difference and measurement error," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    7. Xiangang Cao & Pengfei Li & Song Ming, 2021. "Remaining Useful Life Prediction-Based Maintenance Decision Model for Stochastic Deterioration Equipment under Data-Driven," Sustainability, MDPI, vol. 13(15), pages 1-19, July.
    8. Prakash, Om & Samantaray, Arun Kumar, 2021. "Prognosis of Dynamical System Components with Varying Degradation Patterns using model–data–fusion," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    9. Pedersen, Tom Ivar & Liu, Xingheng & Vatn, Jørn, 2023. "Maintenance optimization of a system subject to two-stage degradation, hard failure, and imperfect repair," Reliability Engineering and System Safety, Elsevier, vol. 237(C).

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