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Implicit Kalman filtering method for remaining useful life prediction of rolling bearing with adaptive detection of degradation stage transition point

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  • Li, Guofa
  • Wei, Jingfeng
  • He, Jialong
  • Yang, Haiji
  • Meng, Fanning

Abstract

Remaining useful life (RUL) prediction is a vital task in rolling bearing prognostics and health management (PHM) process. Kalman filtering (KF) is one of the hot spots in the research area of RUL prediction. However, three dispiriting shortcomings in KF methods are still unavoidable, including: (1) difficulty in tracking the unknown time-varying noise information, (2) the subjectivity for setting time to start prediction (TSP), and (3) short-term accuracy of the predicting results based on linear predictors. To improve the capability of KF methods, this work adopts the variational Bayesian technique to adaptively describe noise information and considers linear and nonlinear factors of multi-channel signals to recognize the degradation stage transition point of bearing as TSP. Moreover, this work proposes an implicit Kalman filtering method to predict the RUL. The effectiveness of the proposed method is validated on XJTU-SY and IMS-Rexnord bearing data. Results show that the proposed method can recognize the TSP and improve the long-term accuracy of the prediction result during the accelerated degradation stage.

Suggested Citation

  • Li, Guofa & Wei, Jingfeng & He, Jialong & Yang, Haiji & Meng, Fanning, 2023. "Implicit Kalman filtering method for remaining useful life prediction of rolling bearing with adaptive detection of degradation stage transition point," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    • Handle: RePEc:eee:reensy:v:235:y:2023:i:c:s0951832023001849
    DOI: 10.1016/j.ress.2023.109269
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    References listed on IDEAS

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    1. 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).
    2. Ahmad, Wasim & Khan, Sheraz Ali & Islam, M M Manjurul & Kim, Jong-Myon, 2019. "A reliable technique for remaining useful life estimation of rolling element bearings using dynamic regression models," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 67-76.
    3. Long, Junqi & Chen, Chuanhai & Liu, Zhifeng & Guo, Jinyan & Chen, Weizheng, 2022. "Stochastic hybrid system approach to task-orientated remaining useful life prediction under time-varying operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    4. Kumar, Anil & Parkash, Chander & Vashishtha, Govind & Tang, Hesheng & Kundu, Pradeep & Xiang, Jiawei, 2022. "State-space modeling and novel entropy-based health indicator for dynamic degradation monitoring of rolling element bearing," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    5. Liu, Shujie & Fan, Lexian, 2022. "An adaptive prediction approach for rolling bearing remaining useful life based on multistage model with three-source variability," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    6. Jinjiang Wang & Robert X. Gao & Zhuang Yuan & Zhaoyan Fan & Laibin Zhang, 2019. "A joint particle filter and expectation maximization approach to machine condition prognosis," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 605-621, February.
    7. Wang, Han & Wang, Dongdong & Liu, Haoxiang & Tang, Gang, 2022. "A predictive sliding local outlier correction method with adaptive state change rate determining for bearing remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
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    Cited by:

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    5. Wang, Chenyushu & Cai, Baoping & Shao, Xiaoyan & Zhao, Liqian & Sui, Zhongfei & Liu, Keyang & Khan, Javed Akbar & Gao, Lei, 2023. "Dynamic risk assessment methodology of operation process for deepwater oil and gas equipment," Reliability Engineering and System Safety, Elsevier, vol. 239(C).

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