Journal of Jilin University(Engineering and Technology Edition) ›› 2024, Vol. 54 ›› Issue (10): 3009-3017.doi: 10.13229/j.cnki.jdxbgxb.20221537

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Bearing fault diagnosis based on attention for multi-scale convolutional neural network

Xi-jun ZHANG(),Ji-yang SHANG,Guang-jie YU,Jun HAO   

  1. School of Computer and Communication，Lanzhou University of Technology，Lanzhou 730050，China
  • Received:2022-12-01 Online:2024-10-01 Published:2024-11-22

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Abstract:

Aiming at the problem of low accuracy of bearing fault diagnosis based on convolution neural network in noisy environment， a multi-scale convolution neural network anti-noise model （MACNN） with channel attention was proposed. When extracting features of different scales， the model adaptively selected channels containing fault features by using channel attention to improve the anti-noise ability of the model and suppressed the influence of noise； In addition， the one-dimensional convolution of adaptive size was used to adjust the channel weights of features of different scales， and the features of different scales were adaptively fused； Finally， feature classification is carried out through full connection layer. Experimental results on two bearing datasets show that MACNN has better fault diagnosis ability than other methods under noise interference with different signal-to-noise ratios.

Key words: fault diagnosis, rolling bearing, convolutional neural network, attention mechanism

CLC Number: 

  • TP277

Fig.1

Channel attention mechanism"

Fig.2

MACNN model structure"

Table 1

MACNN network parameters"

层的名称核尺寸核数步长
宽卷积层64×1322
池化层2×1322
多尺度卷积层15×1/7×1/9×164/64/641/1/1
多尺度池化层12×1/2×1/2×164/64/642/2/2
注意力层1—64/64/64—
多尺度卷积层25×1/7×1/9×1128/128/1281/1/1
多尺度池化层22×1128/128/1282/2/2
注意力层2—128/128/128—
全局平均池化———
全连接层———

Fig.3

The flow chart of MACNN for fault diagnosis"

Fig.4

Rolling bearing test bench"

Table 2

0 hp Rolling bearing dataset"

分类类别故障位置故障直径/mm
0健康—
1内圈0.177 8
2内圈0.355 6
3内圈0.553 4
4外圈0.177 8
5外圈0.355 6
6外圈0.553 4
7滚动体0.177 8
8滚动体0.355 6
9滚动体0.553 4

Fig.5

Original fault signal and fault signal under noise"

Fig.6

Visualization of model training process"

Fig.7

Effect of attention mechanism"

Fig.8

Comparison of different methods under noise"

Table 3

Comparison of different methods under noise"

诊断方法-5 dB-3 dB-1 dB1 dB
WDCNN25.62±2.2334.84±3.9856.83±3.9474.79±2.07
MC-CNN54.69±2.2075.88±1.2580.57±2.6192.71±1.95
ResNet71.23±2.0185.99±2.6690.10±1.2096.25±1.36
MCNN71.77±3.4886.61±1.4192.24±1.1995.52±1.27
MACNN88.80±2.2196.51±1.6398.53±0.5999.58±0.41

Fig.9

Different methods of variable load diagnosis"

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