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A framework for predicting the remaining useful life of a single unit under time-varying operating conditions

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  • Haitao Liao
  • Zhigang Tian

Abstract

Product reliability in the field is important for a wide variety of critical applications such as manufacturing, transportation, power generation, and health care. In particular, the propensity of achieving zero-downtime emphasizes the need for Remaining Useful Life (RUL) prediction for a single unit. The task is quite challenging when the unit is subject to time-varying operating conditions. This article provides a framework for predicting the RUL of a single unit under time-varying operating conditions by incorporating the results of both accelerated degradation testing and in situ condition monitoring. For illustration purposes, the underlying degradation process is modeled as a Brownian motion evolving in response to the operating conditions. The model is combined with in situ degradation measurements of the unit and the operating conditions to predict the unit's RUL through a Bayesian technique. When the operating conditions are piecewise constant, statistical approaches using a conjugate prior distribution and Markov chain Monte Carlo approach are developed for cases involving linear and non-linear degradation–stress relationships, respectively. The proposed framework is also extended to handle a more complex case where the projected future operating conditions are stochastic. Simulation experiments and a case study for ball bearings are used to verify the prediction capability and practicality of the framework. In the case study, a quantile regression technique is proposed to handle load-dependent failure threshold values in RUL prediction.

Suggested Citation

  • Haitao Liao & Zhigang Tian, 2013. "A framework for predicting the remaining useful life of a single unit under time-varying operating conditions," IISE Transactions, Taylor & Francis Journals, vol. 45(9), pages 964-980.
  • Handle: RePEc:taf:uiiexx:v:45:y:2013:i:9:p:964-980
    DOI: 10.1080/0740817X.2012.705451
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    Citations

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    Cited by:

    1. Jiawen Hu & Zuhua Jiang & Haitao Liao, 2017. "Preventive maintenance of a batch production system under time-varying operational condition," International Journal of Production Research, Taylor & Francis Journals, vol. 55(19), pages 5681-5705, October.
    2. Langeron, Y. & Grall, A. & Barros, A., 2015. "A modeling framework for deteriorating control system and predictive maintenance of actuators," Reliability Engineering and System Safety, Elsevier, vol. 140(C), pages 22-36.
    3. Ye, Zhi-Sheng & Chen, Nan & Shen, Yan, 2015. "A new class of Wiener process models for degradation analysis," Reliability Engineering and System Safety, Elsevier, vol. 139(C), pages 58-67.
    4. Mukhopadhyay, Koushiki & Liu, Bin & Bedford, Tim & Finkelstein, Maxim, 2023. "Remaining lifetime of degrading systems continuously monitored by degrading sensors," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    5. Dan Xu & Jiaolan He & Zhou Yang, 2022. "Reliability prediction based on Birnbaum–Saunders model and its application to smart meter," Annals of Operations Research, Springer, vol. 312(1), pages 519-532, May.
    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. Chen, Nan & Ye, Zhi-Sheng & Xiang, Yisha & Zhang, Linmiao, 2015. "Condition-based maintenance using the inverse Gaussian degradation model," European Journal of Operational Research, Elsevier, vol. 243(1), pages 190-199.
    8. Chen, Wen-Bin & Li, Xiao-Yang & Kang, Rui, 2022. "Integration for degradation analysis with multi-source ADT datasets considering dataset discrepancies and epistemic uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    9. Wang, Han & Liao, Haitao & Ma, Xiaobing & Bao, Rui, 2021. "Remaining Useful Life Prediction and Optimal Maintenance Time Determination for a Single Unit Using Isotonic Regression and Gamma Process Model," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    10. Linmiao Zhang & Yong Lei & Houcai Shen, 2016. "How heterogeneity influences condition-based maintenance for gamma degradation process," International Journal of Production Research, Taylor & Francis Journals, vol. 54(19), pages 5829-5841, October.
    11. Hu, Jiawen & Jiang, Zuhua & Liao, Haitao, 2017. "Preventive maintenance of a single machine system working under piecewise constant operating condition," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 105-115.
    12. Peng, Weiwen & Li, Yan-Feng & Mi, Jinhua & Yu, Le & Huang, Hong-Zhong, 2016. "Reliability of complex systems under dynamic conditions: A Bayesian multivariate degradation perspective," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 75-87.
    13. Li, Naipeng & Gebraeel, Nagi & Lei, Yaguo & Bian, Linkan & Si, Xiaosheng, 2019. "Remaining useful life prediction of machinery under time-varying operating conditions based on a two-factor state-space model," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 88-100.
    14. Peihua Jiang, 2022. "Statistical Inference of Wiener Constant-Stress Accelerated Degradation Model with Random Effects," Mathematics, MDPI, vol. 10(16), pages 1-18, August.
    15. 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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