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A general time-varying Wiener process for degradation modeling and RUL estimation under three-source variability

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  • Wang, Yu
  • Liu, Qiufa
  • Lu, Wenjian
  • Peng, Yizhen

Abstract

The study of the remaining useful life (RUL) has shown a rising momentum for ensuring system availability in past few years. For an engineering unit, due to the existence of dynamic operational condition and unpredictable internal degradation mechanisms, the degradation process tends to exhibit a multi-phase pattern, where the degradation rate and variation level are varied at different stages. In conventional degradation models, there are challenges in tracking the dynamic and multi-source variability of a degradation process jointly. To get a more adaptable and robust RUL estimation, a general time-varying Wiener process (GTWP) is proposed in this paper. First, a state-space model is constructed to consider nonlinearity and three-source variability simultaneously, where an implicit transition model is incorporated to depict the evolution of model parameters over time. Then an approximate analytical form for the estimated RUL is derived under the concept of the first hitting time (FHT). At the offline stage, a two-step approach is developed to identify unknown model parameters for the usability. To verify the feasibility and superiority of the proposed model, three simulation cases and the XJTU-SY bearing dataset are adopted. The results show that the proposed model is more general and owns higher accuracy and faster convergence on most units compared with exiting homogeneous models.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:232:y:2023:i:c:s0951832022006561
    DOI: 10.1016/j.ress.2022.109041
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    References listed on IDEAS

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    6. 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).
    7. Zhang, Jiusi & Jiang, Yuchen & Li, Xiang & Huo, Mingyi & Luo, Hao & Yin, Shen, 2022. "An adaptive remaining useful life prediction approach for single battery with unlabeled small sample data and parameter uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
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    Cited by:

    1. Hou, WanJun & Peng, Yizhen, 2023. "Adaptive ensemble gaussian process regression-driven degradation prognosis with applications to bearing degradation," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    2. Yang, Shilong & Tang, Baoping & Wang, Weiying & Yang, Qichao & Hu, Cheng, 2024. "Physics-informed multi-state temporal frequency network for RUL prediction of rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    3. 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).

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