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Remaining useful life prediction of individual units subject to hard failure

Author

Listed:
  • Qiang Zhou
  • Junbo Son
  • Shiyu Zhou
  • Xiaofeng Mao
  • Mutasim Salman

Abstract

To develop a cost-effective condition-based maintenance strategy, accurate prediction of the Remaining Useful Life (RUL) is the key. It is known that many failure mechanisms in engineering can be traced back to some underlying degradation processes. This article proposes a two-stage prognostic framework for individual units subject to hard failure, based on joint modeling of degradation signals and time-to-event data. The proposed algorithm features a low computational load, online prediction, and dynamic updating. Its application to automotive battery RUL prediction is discussed in this article as an example. The effectiveness of the proposed method is demonstrated through a simulation study and real data.

Suggested Citation

  • Qiang Zhou & Junbo Son & Shiyu Zhou & Xiaofeng Mao & Mutasim Salman, 2014. "Remaining useful life prediction of individual units subject to hard failure," IISE Transactions, Taylor & Francis Journals, vol. 46(10), pages 1017-1030, October.
    • Handle: RePEc:taf:uiiexx:v:46:y:2014:i:10:p:1017-1030
    DOI: 10.1080/0740817X.2013.876126
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    Citations

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    Cited by:

    1. Son, Junbo & Zhou, Shiyu & Sankavaram, Chaitanya & Du, Xinyu & Zhang, Yilu, 2016. "Remaining useful life prediction based on noisy condition monitoring signals using constrained Kalman filter," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 38-50.
    2. Xu, Huyang & Fard, Nasser & Fang, Yuanchen, 2020. "Time series chain graph for modeling reliability covariates in degradation process," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    3. Deep, Akash & Zhou, Shiyu & Veeramani, Dharmaraj & Chen, Yong, 2023. "Partially observable Markov decision process-based optimal maintenance planning with time-dependent observations," European Journal of Operational Research, Elsevier, vol. 311(2), pages 533-544.
    4. Hu, Jiawen & Chen, Piao, 2020. "Predictive maintenance of systems subject to hard failure based on proportional hazards model," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    5. Brenière, Léa & Doyen, Laurent & Bérenguer, Christophe, 2020. "Virtual age models with time-dependent covariates: A framework for simulation, parametric inference and quality of estimation," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    6. Jahani, Salman & Zhou, Shiyu & Veeramani, Dharmaraj, 2021. "Stochastic prognostics under multiple time-varying environmental factors," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    7. Pang, Zhenan & Si, Xiaosheng & Hu, Changhua & Du, Dangbo & Pei, Hong, 2021. "A Bayesian Inference for Remaining Useful Life Estimation by Fusing Accelerated Degradation Data and Condition Monitoring Data," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    8. Fallahdizcheh, Amirhossein & Wang, Chao, 2022. "Transfer learning of degradation modeling and prognosis based on multivariate functional analysis with heterogeneous sampling rates," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    9. Zhang, Zhengxin & Si, Xiaosheng & Hu, Changhua & Lei, Yaguo, 2018. "Degradation data analysis and remaining useful life estimation: A review on Wiener-process-based methods," European Journal of Operational Research, Elsevier, vol. 271(3), pages 775-796.

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