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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2015  >  15(3): 78-84, 100
CAO Yuan, WANG Yu-jue, MA Lian-chuan, CHEN Lei. Monitoring method of vehicle axle temperature based on dynamic time warping[J]. Journal of Traffic and Transportation Engineering, 2015, 15(3): 78-84, 100. doi: 10.19818/j.cnki.1671-1637.2015.03.009
Citation: CAO Yuan, WANG Yu-jue, MA Lian-chuan, CHEN Lei. Monitoring method of vehicle axle temperature based on dynamic time warping[J]. Journal of Traffic and Transportation Engineering, 2015, 15(3): 78-84, 100. doi: 10.19818/j.cnki.1671-1637.2015.03.009
shu

Monitoring method of vehicle axle temperature based on dynamic time warping

doi: 10.19818/j.cnki.1671-1637.2015.03.009
  • 1.

    National Engineering Research Center of Rail Traffic Operation and Control System, Beijing Jiaotong University, Beijing 100044, China

  • 2.

    School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China

  • 3.

    School of Electronic, Electrical and Systems Engineering, University of Birmingham, Birmingham B15 2TT, West Midlands, UK

More Information
  • Author Bio:

    CAO Yuan(1982-), male, associate professor, PhD, +86-10-51686121, ycao@bjtu.edu.cn

  • Received Date: 2015-02-21
  • Publish Date: 2015-06-20
    Abstract
  • In order to handle with the abnormal data of vehicle axle temperature, such as jump, deletion and noise, a monitoring method of vehicle axle temperature based on dynamic time warping method was put forward to reduce the false alarm rate. The historical monitoring data and historical statistical data were preprocessed by using exponential smoothing method. At the training stage, the data were iterated to get the reference samples of different axle temperature modes. The frame matching distance matrix was obtained by computing Euclidean distances of data frames between real-time axle temperatures and reference samples. With the idea of dynamic programming and backtracking, the cumulative distance matrix and dynamic time warping path were calculated. The dynamic time warping distance was taken as the quantitative similarity index of two time series to the corresponding axle temperature mode for the minimum dynamic time warping distance, thus the axle temperature condition was achieved. Simulation result shows that when 1 000 test samples of axle temperature with the time ranges of 50 min to 300 min are inputted in MATLAB, the maximum response time is less than 0.4 s, there are 29 false matches, and the false alarm rate is below 3~. The jump interferences of axle temperature are effectively eliminated by processing the data using exponential smoothing method. The values and numbers of axle temperature jumps are different, but the relative dynamic time warping distances are invariable. Obviously, the method can meet the real-time and accuracy requirements of vehicle axle temperature monitoring and reduces the false alarm rate.

     

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