|Table of Contents|

Monitoring method of vehicle axle temperature based on dynamic time warping(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2015年03期
Page:
78-84,100
Research Field:
载运工具运用工程
Publishing date:

Info

Title:
Monitoring method of vehicle axle temperature based on dynamic time warping
Author(s):
CAO Yuan1; 2;  WANG Yu-jue2;  MA Lian-chuan1; 2;  CHEN Lei3
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
Keywords:
vehicle engineering;  axle temperature monitoring;  dynamic time warping method;  exponential smoothing method
PACS:
U270.7
DOI:
-
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. 3 tabs, 6 figs, 21 refs.

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Last Update: 2015-06-20