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<p>In the prediction of the Remaining Useful Life (RUL) of rolling bearings, determining the First Predicting Time (FPT) for bearing life under different working conditions is challenging, leading to low prediction accuracy. To address this issue, this paper proposes a method based on Transfer Fuzzy C-Means-Relative Membership Difference (TFCM-RMD) for classifying health states and selecting appropriate FPTs. The Stochastic Configuration Networks (SCNs) model is employed to predict the RUL of rolling bearings. Firstly, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is utilized to decompose the original vibration signal of the bearing and extract its features. Secondly, due to the fluctuation of the health index during degradation, there exists a fuzzy clustering boundary problem between different states of rolling bearings. Therefore, the TFCM-RMD method is adopted in this study. By quantifying the difference in membership degrees, the FPT for bearings under various working conditions is selected. Finally, the SCNs prediction model is established using data after the FPT. In this study, the proposed method is experimentally validated using PHM2012 challenge data and compared with other prediction methods. The experimental results demonstrate that the proposed method not only achieves accurate classification of bearing health states under different working conditions but also improves the RUL prediction scores of rolling bearings by 6%, 5%, 18%, 13%, and 10%, respectively, compared to other methods. This approach enables earlier detection of bearing anomalies and holds practical significance for predictive maintenance.</p>

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Remaining useful life prediction of rolling bearing with determined first predicting time by transfer clustering learning and stochastic configuration networks

  • 期刊论文

  • 发表日期：

    2025年7月3日

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  • Ying Han,
  • Jiaqi Feng,
  • Kun Li

摘要

In the prediction of the Remaining Useful Life (RUL) of rolling bearings, determining the First Predicting Time (FPT) for bearing life under different working conditions is challenging, leading to low prediction accuracy. To address this issue, this paper proposes a method based on Transfer Fuzzy C-Means-Relative Membership Difference (TFCM-RMD) for classifying health states and selecting appropriate FPTs. The Stochastic Configuration Networks (SCNs) model is employed to predict the RUL of rolling bearings. Firstly, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is utilized to decompose the original vibration signal of the bearing and extract its features. Secondly, due to the fluctuation of the health index during degradation, there exists a fuzzy clustering boundary problem between different states of rolling bearings. Therefore, the TFCM-RMD method is adopted in this study. By quantifying the difference in membership degrees, the FPT for bearings under various working conditions is selected. Finally, the SCNs prediction model is established using data after the FPT. In this study, the proposed method is experimentally validated using PHM2012 challenge data and compared with other prediction methods. The experimental results demonstrate that the proposed method not only achieves accurate classification of bearing health states under different working conditions but also improves the RUL prediction scores of rolling bearings by 6%, 5%, 18%, 13%, and 10%, respectively, compared to other methods. This approach enables earlier detection of bearing anomalies and holds practical significance for predictive maintenance.