Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (1): 53-62.doi: 10.13229/j.cnki.jdxbgxb20200751

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State recognition in bearing temperature of high-speed train based on machine learning algorithms

Liang DUAN(),Chun-yuan SONG,Chao LIU,Wei WEI,Cheng-ji LYU   

  1. National Railway Vehicle Engineering R&D Center，CRRC Changchun Railway Vehicles Co. ，Ltd. ，Changchun 130002，China
  • Received:2020-07-10 Online:2022-01-01 Published:2022-01-14

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Abstract:

By studying a large number of historical data of high-speed train bearing temperature， the opinion， which the trend among related measuring positions of bearing temperature are similar， was used to identify the state of bearing temperature. Firstly the unsupervised learning algorithm One-class SVM was applied to abnormality detection. The result shows that the model could detect the abnormality well but not identify the state consistently. Then the whole process， which meant the bearing worked from normal to failure according to the relative temperature， was divided into four phases， the long， medium， short and urgent phases. The way， which describes the trend of bearing temperature through image including the information of relative temperature and the changing rate of temperature， was proposed. Finally， the deep learning algorithm CNN was used to identify the bearing temperature state consistently. The result shows that the CNN model could reliably identify the state of bearing temperature， and this could provide some support for predictive maintenance.

Key words: vehicle engineering, bearing temperature state, convolutional neural network, image classification, high-speed train, machine learning

CLC Number: 

  • U271

Fig.1

Measuring position of axle box bearing"

Fig.2

Temperature of related position of normal bearing"

Fig.3

Temperature of related position of abnormal bearing"

Fig.4

Disassemble of abnormal bearing"

Table 1

Rule in labelling temperature state"

距故障发生/天温度状态标签
0~3紧急（含失效）
4~10短
11~31中
32~inf长

Fig.5

Historical temperature of related position in training set including label"

Fig.6

Historical temperature of related position in testing set including label"

Fig.7

Result of One-class hyperplane in training set"

Fig.8

Result of One-class hyperplane in testing set"

Fig.9

Temperature of related position of normal bearing in time domain"

Fig.10

Temperature of related position of abnormal bearing in time domain"

Fig.11

Relative temperature and changing rate of abnormal bearing before failure"

Fig.12

Relative temperature and changing rate of normal bearing before failure"

Fig.13

Data sample of related position in time domain"

Fig.14

Image sample used for identifying bearing temperature state"

Table 2

Rule in defining temperature state"

关联测点之间相对温度/℃温度状态标签
0~15长
15~30中
30~45短
45~inf紧急

Fig.15

Temperature state of two abnormal bearing before failure"

Table 3

Network structure of CNN model"

序号层类型特征图数量特征图大小
1输入层1300×300×3
2卷积层13255×55×32
3池化层13227×27×32
4卷积层26427×27×64
5池化层26413×13×64
6卷积层36413×13×64
7卷积层425613×13×256
8卷积层512813×13×128
9池化层31286×6×128
10全连接层11 0241×1×1 024
11全连接层21 0241×1×1 024
12输出层11×1×4

Fig.16

Accuracy and cross entropy during model training"

Fig.17

Confusion matrix of result of testing set"

Fig.18

Accuracy of CNN model in testing set"

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