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A predictive sliding local outlier correction method with adaptive state change rate determining for bearing remaining useful life estimation

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  • Wang, Han
  • Wang, Dongdong
  • Liu, Haoxiang
  • Tang, Gang

Abstract

The accurate estimation of remaining useful life (RUL) is significant for the operation, maintenance, and avoidance of unplanned downtime of rotating machinery. To improve the prediction accuracy of RUL, this paper proposes a predictive sliding local outlier correction (PSLOC) method with adaptive state change rate (SCR) determining algorithm for bearing RUL prediction. The proposed method addresses some key issues in existing bearing health prognosis methods that affect the prognostic performance. First, the PSLOC method is proposed, which can eliminate the random spurious fluctuations in the degradation process. It not only ensures the integrity of the degradation trend, but also avoids the influence of abnormal fluctuations in the time-series data on the judgment of the first predicting time (FPT) and the RUL prediction accuracy. Second, the proposed SCR algorithm can be actively adjusted according to the changes in the degradation process to achieve an accurate FPT determination. Finally, the simulated degradation algorithm is used to improve the exponential model to reduce the random errors in the degradation process and improve the prediction accuracy. The PRONOSTIA dataset is used as a case study to illustrate the superiority and effectiveness of the proposed method.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:225:y:2022:i:c:s0951832022002460
    DOI: 10.1016/j.ress.2022.108601
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    References listed on IDEAS

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    4. Cao, Yudong & Ding, Yifei & Jia, Minping & Tian, Rushuai, 2021. "A novel temporal convolutional network with residual self-attention mechanism for remaining useful life prediction of rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    5. Ding, Yifei & Jia, Minping & Miao, Qiuhua & Huang, Peng, 2021. "Remaining useful life estimation using deep metric transfer learning for kernel regression," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
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    7. de Pater, Ingeborg & Reijns, Arthur & Mitici, Mihaela, 2022. "Alarm-based predictive maintenance scheduling for aircraft engines with imperfect Remaining Useful Life prognostics," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    8. Hu, Tao & Guo, Yiming & Gu, Liudong & Zhou, Yifan & Zhang, Zhisheng & Zhou, Zhiting, 2022. "Remaining useful life prediction of bearings under different working conditions using a deep feature disentanglement based transfer learning method," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    9. Chen, Zhen & Li, Yaping & Zhou, Di & Xia, Tangbin & Pan, Ershun, 2021. "Two-phase degradation data analysis with change-point detection based on Gaussian process degradation model," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    10. 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.
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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. 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).
    3. Xiong, Jiawei & Zhou, Jian & Ma, Yizhong & Zhang, Fengxia & Lin, Chenglong, 2023. "Adaptive deep learning-based remaining useful life prediction framework for systems with multiple failure patterns," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    4. Ding, Wanmeng & Li, Jimeng & Mao, Weilin & Meng, Zong & Shen, Zhongjie, 2023. "Rolling bearing remaining useful life prediction based on dilated causal convolutional DenseNet and an exponential model," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    5. Bermeo-Ayerbe, Miguel Angel & Cocquempot, Vincent & Ocampo-Martinez, Carlos & Diaz-Rozo, Javier, 2023. "Remaining useful life estimation of ball-bearings based on motor current signature analysis," Reliability Engineering and System Safety, Elsevier, vol. 235(C).

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