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Volume 59 Issue 1
Jan.  2024
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Abstract
References
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Journal of Southwest Jiaotong University  > 2024  >  59(1): 229-238.
WANG Biao, QIN Yong, JIA Limin, CHENG Xiaoqing, ZENG Chunping, GAO Yifan. Monitoring Data-Driven Prediction of Remaining Useful Life of Axle-Box Bearings for Urban Rail Transit Trains[J]. Journal of Southwest Jiaotong University, 2024, 59(1): 229-238. doi: 10.3969/j.issn.0258-2724.20220230
Citation: WANG Biao, QIN Yong, JIA Limin, CHENG Xiaoqing, ZENG Chunping, GAO Yifan. Monitoring Data-Driven Prediction of Remaining Useful Life of Axle-Box Bearings for Urban Rail Transit Trains[J]. Journal of Southwest Jiaotong University, 2024, 59(1): 229-238. doi: 10.3969/j.issn.0258-2724.20220230
shu

Monitoring Data-Driven Prediction of Remaining Useful Life of Axle-Box Bearings for Urban Rail Transit Trains

doi: 10.3969/j.issn.0258-2724.20220230
  • 1.

    State Key Lab of Rail Traffic Control & Safety, Beijing Jiaotong University, Beijing 100044, China

  • 2.

    Beijing Jinhong Xi-Dian Information Technology Co., Ltd., Beijing 100071, China

  • Received Date: 31 Mar 2022
  • Rev Recd Date: 03 Jul 2022
    • Available Online: 17 Dec 2022
  • Publish Date: 07 Jul 2022
    Abstract

  • The operating conditions of axle-box bearings of urban rail transit trains are complex and time-varying, and they often suffer from random external interferences. Correspondingly, the monitoring data of axle-box bearings contain a great amount of measurement noise and even abnormal data, thereby limiting the accuracy of prognostics models. To overcome the aforementioned problems, a monitoring data-driven dynamic multiple aggregation prediction method is proposed for forecasting the remaining useful life (RUL) of axle-box bearings of urban rail transit trains. In the proposed method, abnormal data are first automatically recognized and deleted by measuring the amplitude distribution similarity between signals in a short time. Then, various degradation curves can be fitted to predict the mean and variance of RUL by aggregating health indicators from different temporal scales. The proposed method is evaluated using vibration data from real monitoring systems of urban rail transit trains and accelerated degradation tests of rolling element bearings. The results show that the proposed method is able to effectively recognize the not a number (NaN) data and strong interference data, and as time goes on, the predictive RUL converges to the actual RUL gradually and the 95% confidence interval becomes narrower. Further, compared with the single exponential prognostics model and the hybrid prognostics model, the proposed method increases the mean of cumulative relative accuracy by 29.78% and 27.63% respectively, and improves the mean of convergence speed by 10.56% and 10.20% respectively.

     

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