Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (2): 474-482.doi: 10.13229/j.cnki.jdxbgxb20210644

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Fault diagnosis of high⁃speed train axle bearing based on a lightweight neural network Shuffle⁃SENet

Fei-yue DENG1,2(), LYUHao-yang2,Xiao-hui GU1(),Ru-jiang HAO2   

  1. 1.State Key Laboratory of Mechanical Behavior in Traffic Engineering Structure and System Safety,Shijiazhuang Tiedao University,Shijiazhuang 050043,China
    2.School of Mechanical Engineering,Shijiazhuang Tiedao University,Shijiazhuang 050043,China
  • Received:2021-07-08 Online:2022-02-01 Published:2022-02-17
  • Contact: Xiao-hui GU E-mail:dengfy@stdu.edu.cnail;guxh@stdu.edu.cnail

Abstract:

Aiming at the problem of difficult to accurately diagnose axle bearing faults in high-speed train under complex operating conditions, this paper proposes a novel design method named Shuffle-SE neural network unit to address this issue. A lightweight neural network called Shuffle-SENet is developed to diagnose high-speed train axle bearing fault on this foundation. The proposed Shuffle-SENet unit is based on the ShufflNet V2 unit. It carries out local optimization of the network structure while retaining lightweight frame, and further integrates the Squeeze-and-Exception(SE) network structure. The proposed network model reduces the need for complex computations while making network operation more efficient and fault diagnosis accuracy significantly improved. In addition, the influence of the Shuffle-SE unit numbers and reduction dimension coefficient of SE unit on the performance of the proposed model is also analyzed in this paper. The experimental results that the proposed method canbe effectively used for fault diagnosis of axle box bearing of the high-speed train under various complex conditions. Compared with the lightweight network models such as MobileNet V2, Shufflenet V1/V2, ResNets, the proposed method guarantees the high efficiency of network operation and greatly improves the diagnostic accuracy of model fault diagnosis. This paper provides a new solution for deep learning technology to be applied in engineering practice and overcome the limitation of high computer hardware demand。

Key words: fault diagnosis, axle bearing, high-speed train, lightweight neural network, ShuffleNet unit

CLC Number: 

  • TH17

Fig.1

Channel shuffle operation process"

Fig.2

Two basic units of ShuffleNet V2"

Fig.3

Architecture of SE unit"

Fig.4

Architecture of Shuffle-SE unit"

Fig.5

Architecture of the proposed network model"

Fig.6

Single bogie rolling-vibration test rig of high speed train"

Fig.7

Axle bearing time-domain waveforms of different health states at 50 km/h"

Fig.8

Classification accuracy of proposed network for original signals"

Fig.9

Classification loss of proposed network for original signals"

Table 1

Comparison results of different unit numbers under -8 dB noise"

单元

数量

模型训练

时间/s

模型参

数量/105

FLOPs/105

测试精

度/%

217100.250.5055.78
321960.941.8962.14
430613.637.2760.92
5501914.2628.5260.64

Table 2

Comparison results of different dimensionality reduction ratios under -8 dB noise"

r

模型训练

时间/s

模型参

数量/105

FLOPs/105

测试精

度/%

421960.941.8962.14
821650.731.4560.97
1621080.621.2460.36
3220910.561.1360.94
6420870.541.0859.83

Table 3

Comparison result of operation efficiency and complexity of different methods"

信噪比/dB方法模型训练时间/s模型参数量/105FLOPs/105
-8MobileNet V21 9430.701.41
ShuffleNet V15 5480.831.66
ShuffleNet V22 9450.731.45
ResNets12 41624.0247.99
本文方法2 1960.941.89
-5MobileNet V21 8940.701.41
ShuffleNet V15 3990.831.66
ShuffleNet V22 9280.731.45
ResNets12 46424.0247.99
本文方法2 1120.941.89
-3MobileNet V21 9630.701.41
ShuffleNet V15 6460.831.66
ShuffleNet V22 9120.731.45
ResNets12 74124.0247.99
本文方法2 1340.941.89
原信号MobileNet V21 8990.701.41
ShuffleNet V15 4490.831.66
ShuffleNet V12 8800.731.45
ResNets12 46224.0247.99
本文方法2 1210.941.89

Fig.10

Comparison of accuracy testing on signal samples with different noise strengths"

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