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A nonlinear-drift-driven Wiener process model for remaining useful life estimation considering three sources of variability

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  • Yu, Wennian
  • Tu, Wenbing
  • Kim, Il Yong
  • Mechefske, Chris

Abstract

The estimation of remaining useful life (RUL) for a degrading system has gained increasing attention both in academia and in industry for decades. In this study, a nonlinear-drift-driven Wiener process model considering three common sources of uncertainty is constructed by an age-dependent state-space model for the RUL estimation of degrading systems. Analytical expressions approximating the probability distribution function of RUL of the above-described model are derived for both online estimation and offline estimation scenarios. It is shown that the derived expressions are more general and cover the simplified cases discussed in previous woks. A model parameter estimation method is proposed based on unbalanced historical degradation measurements, and a down-sampling strategy is introduced to avert the underflow issue. The prognostic performance of the proposed method against previous similar works under the online and offline estimation scenarios is demonstrated on two publicly available datasets by comparisons in terms of three prognostic metrics and the probabilistic distribution of RUL at different condition monitoring points. The results show that it is necessary to include the nonlinear degradation characteristics and the three sources of uncertainty into the RUL estimation especially for the offline estimation scenario.

Suggested Citation

  • Yu, Wennian & Tu, Wenbing & Kim, Il Yong & Mechefske, Chris, 2021. "A nonlinear-drift-driven Wiener process model for remaining useful life estimation considering three sources of variability," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    • Handle: RePEc:eee:reensy:v:212:y:2021:i:c:s0951832021001721
    DOI: 10.1016/j.ress.2021.107631
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    References listed on IDEAS

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    1. 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.
    2. Wen, Yuxin & Wu, Jianguo & Das, Devashish & Tseng, Tzu-Liang(Bill), 2018. "Degradation modeling and RUL prediction using Wiener process subject to multiple change points and unit heterogeneity," Reliability Engineering and System Safety, Elsevier, vol. 176(C), pages 113-124.
    3. Yu, Wennian & Kim, II Yong & Mechefske, Chris, 2020. "An improved similarity-based prognostic algorithm for RUL estimation using an RNN autoencoder scheme," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
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    Cited by:

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    2. Xu, Danyang & Qiu, Haobo & Gao, Liang & Yang, Zan & Wang, Dapeng, 2022. "A novel dual-stream self-attention neural network for remaining useful life estimation of mechanical systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    3. Yu, Wennian & Shao, Yimin & Xu, Jin & Mechefske, Chris, 2022. "An adaptive and generalized Wiener process model with a recursive filtering algorithm for remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    4. Wang, Yu & Liu, Qiufa & Lu, Wenjian & Peng, Yizhen, 2023. "A general time-varying Wiener process for degradation modeling and RUL estimation under three-source variability," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    5. Chen, Chuanhai & Li, Bowen & Guo, Jinyan & Liu, Zhifeng & Qi, Baobao & Hua, Chunlei, 2022. "Bearing life prediction method based on the improved FIDES reliability model," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
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    7. Zhang, Ao & Wang, Zhihua & Bao, Rui & Liu, Chengrui & Wu, Qiong & Cao, Shihao, 2023. "A novel failure time estimation method for degradation analysis based on general nonlinear Wiener processes," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    8. Hachem, Hassan & Vu, Hai Canh & Fouladirad, Mitra, 2024. "Different methods for RUL prediction considering sensor degradation," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    9. Jia, Xiang & Guo, Bo, 2022. "Reliability analysis for complex system with multi-source data integration and multi-level data transmission," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    10. Zheng, Bokai & Chen, Cen & Lin, Yigang & Hu, Yifan & Ye, Xuerong & Zhai, Guofu & Zio, Enrico, 2022. "Optimal design of step-stress accelerated degradation test oriented by nonlinear and distributed degradation process," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    11. Aizpurua, J.I. & Stewart, B.G. & McArthur, S.D.J. & Penalba, M. & Barrenetxea, M. & Muxika, E. & Ringwood, J.V., 2022. "Probabilistic forecasting informed failure prognostics framework for improved RUL prediction under uncertainty: A transformer case study," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    12. Lin, Wenyi & Chai, Yi & Fan, Linchuan & Zhang, Ke, 2024. "Remaining useful life prediction using nonlinear multi-phase Wiener process and variational Bayesian approach," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    13. Pang, Zhenan & Li, Tianmei & Pei, Hong & Si, Xiaosheng, 2023. "A condition-based prognostic approach for age- and state-dependent partially observable nonlinear degrading system," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    14. Liu, Junqiang & Yu, Zhuoqian & Zuo, Hongfu & Fu, Rongchunxue & Feng, Xiaonan, 2022. "Multi-stage residual life prediction of aero-engine based on real-time clustering and combined prediction model," Reliability Engineering and System Safety, Elsevier, vol. 225(C).

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