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XIE Guo, WANG Zhu-xin, HEI Xin-hong, GAO Qiao-sheng, WANG Yue-kuan. Axle temperature threshold prediction model of high-speed train for hot axle fault[J]. Journal of Traffic and Transportation Engineering, 2018, 18(3): 129-137. doi: 10.19818/j.cnki.1671-1637.2018.03.013
Citation: XIE Guo, WANG Zhu-xin, HEI Xin-hong, GAO Qiao-sheng, WANG Yue-kuan. Axle temperature threshold prediction model of high-speed train for hot axle fault[J]. Journal of Traffic and Transportation Engineering, 2018, 18(3): 129-137. doi: 10.19818/j.cnki.1671-1637.2018.03.013
shu

Axle temperature threshold prediction model of high-speed train for hot axle fault

doi: 10.19818/j.cnki.1671-1637.2018.03.013
  • 1.

    Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi'an University of Technology, Xi'an 710048, Shaanxi, China

  • 2.

    Department of Computer Engineering, Nihon University, Funabashi 274-8501, Chiba, Japan

More Information
  • Author Bio:

    XIE Guo(1982-), male, professor, PhD, guoxie@xaut.edu.cn

  • Received Date: 2017-12-15
  • Publish Date: 2018-06-25
    Abstract
  • Aiming at the problem that the adaptability of existing fault detection system based on the fixed temperature threshold for the axle was poor, and its high false and missing alarm rate, considering the influence of train speed, environment temperature and running conditions on the axle temperature and the relationship among the factors, a dynamic threshold prediction model for the axle temperature of high-speed train was established. Considering the difference in axle temperature variation of high-speed train under different running conditions, the train running state was divided into three stages: acceleration, steady running and deceleration, and aiming at each stage, the Pearson correlation coefficient method was used to analyze the correlation degree between the axle temperature and original monitoring data of train speed, environment temperature and load, as well as that between the axle temperature and derivative data of runningtime and initial axle temperature. The factors closely related to axle temperature variation were extracted, the multiple regression analysis method was used to establish a dynamic threshold prediction model for axle temperature based on the original monitoring data, and a modified dynamic threshold prediction model based on the original monitoring data and derived data for the three running stages of the train. The models were validated using the Ftest method. The model accuracy was verified based on the measured axle temperature data from high-speed trains in China. Research result shows that in the three stages of acceleration, steady running and deceleration, the average relative errors between the true values of axle temperature and the prediction values of the modified dynamic threshold prediction model are 2.0%, 4.1% and 3.3%, respectively. The prediction accuracies of the modified prediction model in the three stage increase by 79.8%, 64.3%, and 65.6%, respectively, compared to the dynamic threshold prediction model for axle temperature based on the original monitoring data. The decision coefficient of the model is larger than 0.99 and the significance probability is less than 0.05, which indicates that the model is effective.

     

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