Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (11): 2523-2531.doi: 10.13229/j.cnki.jdxbgxb20210374

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Bearing fault diagnosis method under unbalanced data distribution

Jie CAO1,2,3(),Zhi-Dong HE1,Ping YU1,3(),Jin-hua WANG1,3,4   

  1. 1.College of Electrical & Information Engineering，Lanzhou University of Technology，Lanzhou 730050，China
    2.Engineering Research Center of Urban Railway Transportation of Gansu Province，Lanzhou 730050，China
    3.Engineering Research Center of Manufacturing Information of Gansu Province，Lanzhou 730050，China
    4.National Experimental Teaching Center of Electrical and Control Engineering，Lanzhou University of Technology，Lanzhou 730050，China
  • Received:2021-04-26 Online:2022-11-01 Published:2022-11-16
  • Contact: Ping YU E-mail:caoj@lut.edu.cn;yup@lut.edu.cn

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Abstract:

Aiming at the problem that the unbalanced distribution of data in the fault diagnosis of rolling bearings will reduce the model's diagnostic ability， a 1DCNN with Width Kernel of First Layer （WKFL-1DCNN） is proposed. WKFL-1DCNN firstly uses a larger kernel in first-layer to extract fault features， and adds a BN（Batch normalization） layer after the alternate convolution layer to adjust the data distribution； then uses the Class-Balanced loss function instead of the Cross-Entropy loss function to offset the impact of the data imbalanced distribution on the network. Experiments show that the improvement method in this paper can effectively improve the performance of WKFL-1DCNN in unbalanced fault diagnosis， and its fault diagnosis ability is better than other comparison algorithms.

Key words: fault diagnosis, unbalanced data distribution, convolutional neural network, Class-Balance loss function, rolling bearing

CLC Number: 

  • TP277

Fig.1

Structure and parameters of WKFL-1DCNN"

Fig.2

Flow chart of WKFL-1DCNN model"

Fig.3

Bearing failure test bench of CWR"

Table 1

Condition label and number of the samples"

状态及标签训练集样本数测试集样本数
数据集A数据集B数据集C数据集D
正常（9）520500220200
In-0.007（0）52080180200
Ball-0.007（1）52080180200
Out@6-0.007（2）52080180200
In-0.014（3）52030150200
Ball-0.014（4）52030150200
Out@6-0.014（5）52030150200
In-0.021（6）52030150200
Ball-0.021（7）52030150200
Out@6-0.021（8）52030150200
Out@3-0.007（10）52030150200
Out@3-0.021（11）52030150200
Out@12-0.007（12）52030150200
Out@12-0.021（13）52030150200

Table 2

Parameters of networks with different kernels of first-layer"

Layer首层卷积核尺寸
1Conv 64-s8Conv 96-s8Conv 128-s8Conv 64-s8Padding=32
2Conv 9-s2Conv 9-s2Conv 9-s2Conv 9-s2
MaxP 2×2MaxP 2×2MaxP 2×2MaxP 2×2
3Conv 9-s2Conv 8-s2Conv 7-s2Conv 7-s2
MaxP 2×2MaxP 2×2MaxP 2×2MaxP 2×2
4Conv 5-s2Conv 5-s2Conv 5-s2Conv 5-s2
MaxP 2×1MaxP 2×1MaxP 2×1MaxP 2×1
5F 256-50-14F 256-50-14F 256-50-14F 256-50-14
softmaxsoftmaxsoftmaxsoftmax

Table 3

Results of kernel with different sizes"

尺寸宏精准率/%宏召回率/%宏F1-score/%准确率/%
6494.0293.5993.6693.98
9694.6494.5394.5394.87
12895.7195.6195.6395.84
19295.2594.7994.8595.13

Fig.4

Impact of BN layer on classification"

Fig.5

Comparison of diagnosis results between Cross-Entropy function and CBsoftmax function"

Table 4

Classification results of each model on balanced data set"

数据集训练精度/%测试精度/%
WKFL-1DCNN100.099.8
WDCNN100.099.7
MSCNN100.099.5
cRT99.398.2

Fig.6

Classification results of each model on unbalanced data sets"

Table 5

Labels and number of samples of Paderborn dataset"

状态及标签训练集样本数测试集样本数
Normal（0）

1000

160

160

60

60

60

440
KA04+KA22（1）360
KI04+KI14（2）360
KA15（3）300
KA16（4）300
KA30（5）300
KB23（6）

60

60

60

60

60

60

60

300
KB24（7）300
KB27（8）300
KI16（9）300
KI17（10）300
KI18（11）300
KI21（12）300

Table 6

Accuracy of each model on Paderborn dataset"

数据集训练精度/%测试精度/%
WKFL-1DCNN100.094.4
WDCNN100.092.1
MSCNN99.589.5
cRT100.090.9
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