Journal of Jilin University(Engineering and Technology Edition) ›› 2026, Vol. 56 ›› Issue (1): 96-108.doi: 10.13229/j.cnki.jdxbgxb.20240710

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Rolling bearing fault diagnosis based on multi-band feature map and improved SqueezeNet

Zhi-gang FENG1(),Meng-yuan REN1,Bing DONG2,Ming-yue YU1   

  1. 1.School of Automation，Shenyang Aerospace University，Shenyang 110136，China
    2.Aircraft Industry（Group） Co. ，Ltd. ，Shenyang 110000，China
  • Received:2024-06-25 Online:2026-01-01 Published:2026-02-03

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Abstract:

At present， fault diagnosis methods based on deep learning generally have the problems of large model parameters and long diagnosis times， and the diagnostic performance will be greatly reduced in noisy environments. This paper proposes a fast and lightweight intelligent diagnosis model for rolling bearing fault diagnosis. Firstly， the parameters of the variational mode decomposition （VMD） are optimized using the osprey optimization algorithm （OOA） to design a unique multi-frequency band grayscale feature map based on the intrinsic mode function （IMF） component. Then a residual attention mechanism module （RAM） is designed based on the efficient channel attention （ECA） module， which is integrated into the SqueezeNet model， and the K-nearest neighbor （KNN） method is used instead of the Softmax function to identify and classify the faults， and the RSqueezeNet-KNN model is established. Experimental results on two bearing datasets show that the model is able to achieve lightweight applications with excellent diagnostic performance compared to other methods in noisy environments.

Key words: bearing fault diagnosis, grayscale feature map, SqueezeNet model, attention mechanism, light-weighting

CLC Number: 

  • TH17

Fig.1

Structure diagram of SqueezeNet model"

Fig.2

Structure diagram of Fire model"

Fig.3

Diagram of ECA module"

Fig.4

OOA-VMD grayscale feature image construction diagram"

Fig.5

Structure diagram of RAM model"

Fig.6

Structure diagram of RSqueezeNet-KNN model"

Fig.7

Flow diagram of proposed method"

Fig.8

CWRU experimental device schematic diagram"

Table 1

CWRU bearing state data description"

轴承状态直径/英寸标签数据类型样本数
正常—Nor1 024200
滚动体故障0.007Ba11 024200
0.014Ba21 024200
0.021Ba31 024200
内圈故障0.007In11 024200
0.014In21 024200
0.021In31 024200
外圈故障0.007Ou11 024200
0.014Ou21 024200
0.021Ou31 024200

Fig.9

OOA optimizes VMD iterative curve"

Fig.10

Comparison chart of VMD parameter optimization"

Table 2

The best parameter combinations [K,α]in case one"

标签Kα
No172 000
Ba171 994
Ba261 999
Ba361 845
In171 991
In271 998
In371 996
Ou161 940
Ou272 000
Ou371 995

Fig.11

Comparison of time-domain waveforms and spectra of Ba1 after VMD and OOA-VMD processing"

Fig.12

Grayscale feature images of ten state types in case one"

Table 3

Accuracy and training time in different hyperparameter combinations"

超参数组合正确率/%训练时间/s
A99.01145.6
B99.15101.5
C99.0892.8
D99.43150.6
E99.7085.6
F99.5890.5
G96.02149.5
H95.10105.1
I88.7889.0

Table 4

Diagnosis results of models in different proportions of label"

模型标签噪声率（Label noisy rate， LNR）
0%2%5%10%
RSqueezeNet-KNN

100

±0

99.60

±0.27

99.35

±0.40

99.13

±0.4

RSqueezeNet

99.70

±0.21

99.20

±0.32

99.03

±0.41

98.80

±0.51

SqueezeNet

99.35

±0.4

99.13

±0.4

98.93

±0.53

98.80

±0.53

AlexNet

99.35

±0.49

99.16

±0.61

98.75

±0.88

96.53

±1.97

ResNet

98.70

±0.55

98.45

±0.47

98.33

±0.39

97.50

±0.77

WT-IResNet［22］

100

±0

99.59

±0.33

99.30

±0.32

98.66

±0.35

WT-ResNet［22］

99.23

±0.35

98.77

±0.32

97.74

±0.35

95.50

±0.33

Table 5

Comparison of parameters and training time of models"

模型参数/MB训练时间/s
RSqueezeNet-KNN0.01585.849
RSqueezeNet0.01585.6
SqueezeNet0.03195.2
AlexNet24.723143.2
ResNet0.041121.7
WT-IResNet［22］0.132661
WT-ResNet［22］0.148278

Fig.13

Confusion matrices of forecast results in different noise labels"

Fig.14

Comparison of performance of diagnosis models in different noise environments in case one"

Fig.15

Physical diagram of an aero-engine compressor rotor experiment device in case two"

Table 6

Aero-engine compressor rotor tester bearingstate data description"

轴承状态标签数据长度样本数转速/（r·min-1）
正常Norm1 0248001 500
滚动体故障Ball1 0248001 500
内圈故障Inner1 0248001 500
外圈故障Outer1 0248001 500

Table 7

The best parameter combinations [K,α]in case two"

标签Kα
Norm71 999
Ball71 934
Inner71 991
Outer7945

Fig.16

Time-domain waveform and frequency spectrum of Ba1 after OOA-VMD in case two"

Fig.17

Grayscale images of four state types in case two"

Table 8

Diagnosis results of models in differentproportions of label in case two"

模型标签噪声率/LNR
0%2%5%10%
RSqueezeNet-KNN

100

±0

99.97

±0.06

99.91

±0.12

99.89

±0.12

RSqueezeNet

99.95

±0.07

99.87

±0.14

99.84

±0.21

99.75

±0.27

SqueezeNet

99.94

±0.1

99.87

±0.09

98.83

±0.13

99.67

±0.32

AlexNet

99.94

±0.14

99.62

±0.14

99.47

±0.12

99.38

±0.21

ResNet

99.86

±0.08

99.72

±0.13

99.72

±0.13

99.63

±0.17

WT-IResNet［22］

100

±0

99.91

±0.08

99.91

±0.08

99.87

±0.13

WT-ResNet［22］

100

±0

99.89

±0.10

99.89

±0.10

99.82

±0.24

Fig.18

Comparison of performance of diagnosis modelsin different noise environments in case two"

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