1.State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle, Hunan University, Changsha 410082, China 2.College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China
In extremely harsh working environments such as strong impacts, intense radiation and extremely high temperature, the fault modes of mechanical equipment are complex and varied, and it is very difficult to obtain sufficient and effective fault data, even difficult to achieve, so that the accuracy of fault diagnosis is limited, and subsequent maintenance and repair programs are difficult to be effectively developed.To solve this problem, a data enhancement algorithm for multi-discriminator auxiliary classifier generative adversarial network was proposed. By setting up 3 discriminators, 1 generator and adding independent classifier, a new auxiliary classifier generative adversarial network model was constructed. Aiming at the instability issue in the model's training, the Wasserstein distance was introduced to construct a new loss function, and the unilateral soft constraint regularization term with more stability was used to replace the original L2 gradient penalty term to solve the problem of model collapse. Building on this, an efficient channel attention mechanism was adopted to further improve the model's feature extraction capability. The proposed model was applied to extend the fault data set of mechanical equipment to assist the training of deep learning intelligent diagnosis model. Multiple fault data set expansion experiments showed that compared with the existing model, the new model could generate higher quality data, and the accuracy of fault diagnosis was further improved, so it had high application value.
Zihan YE,Zhonghua WANG,Chao JIANG,Xin Lü,Zhe ZHANG. Research on fault diagnosis method based on multi-discriminator auxiliary classifier generative adversarial network. Chinese Journal of Engineering Design, 2024, 31(2): 137-150.
Table 2Data labels and their fault types of experiment 1
Fig.3 Gray scale of partial fault data in experiment 1
Fig.4 Samples generated by different models on CWRU bearing fault data set
模型
类别
0
1
2
3
4
5
6
7
ACGAN
532.10
532.11
532.10
532.11
532.01
532.10
532.11
532.11
ACWGAN-GP
179.08
326.34
488.76
171.87
236.83
199.66
220.80
532.00
MDACGAN
34.31
79.47
220.25
62.28
68.20
82.71
88.68
147.58
Table 3DFI values between real samples and generated samples of CWRU bearing fault data set
模型
类别
0
1
2
3
4
5
6
7
ACGAN
4.330 5
4.873 7
4.661 9
5.337 8
4.445 5
4.761 7
5.637 8
5.071 2
ACWGAN-GP
0.340 6
0.412 0
0.419 7
0.343 7
0.702 4
0.352 2
0.603 9
0.635 6
MDACGAN
0.149 0
0.145 6
0.152 3
0.153 2
0.112 4
0.146 3
0.155 3
0.155 7
Table 4DMM values between real samples and generated samples of CWRU bearing fault data set
Fig.5 Generated samples and training samples t-SNE visualization results of CWRU bearing fault data set
类型
数据集
样本总数/个①
测试集
数据集0
100
训练集
数据集1
50（0）
数据集2
100（50）
数据集3
150（100）
数据集4
200（150）
数据集5
250（200）
数据集6
350（300）
数据集7
200（0）
Table 5Expansion and division of CWRU bearing fault data set
模型
准确率/%
数据集1
数据集2
数据集3
数据集4
数据集5
数据集6
数据集7
ACWGAN-GP
78.37
89.37
92.62
91.12
93.85
94.87
99.75
MDACGAN
78.37
90.25
93.75
95.62
97.13
98.00
99.75
Table 6Classification result of CWRU bearing fault data set
标签
工况
故障类型
失效位置
0
1
Bearing1_1
外圈
1
Bearing1_4
保持架
2
2
Bearing2_1
内圈
3
Bearing2_2
外圈
4
3
Bearing3_3
内圈
5
Bearing3_5
外圈
Table 7Data labels and their fault types of experiment 2
Fig.6 Samples generated by different models on XJTU-SY data set
模型
类别
0
1
2
3
4
5
ACWGAN-GP
270.87
223.04
220.84
69.56
116.89
111.72
MDACGAN
141.37
178.31
144.87
60.16
110.65
66.07
Table 8DFI values between real samples and generated samples of XJTU-SY data set
模型
类别
0
1
2
3
4
5
ACWGAN-GP
0.233 8
0.352 9
0.310 9
0.239 0
0.339 3
0.213 3
MDACGAN
0.164 8
0.198 2
0.160 2
0.115 0
0.178 9
0.122 8
Table 9DMM values between real samples and generated samples of XJTU-SY data set
Fig.7 Generated samples and training samples t-SNE visualization results of XJTU-SY data set
模型
准确率/%
数据集1
数据集2
数据集3
数据集4
数据集5
数据集6
数据集7
ACWGAN-GP
75.00
83.16
83.50
87.33
90.12
92.16
99.66
MDACGAN
75.00
85.86
88.21
89.66
91.83
95.50
99.66
Table 10Classification result of XJTU-SY data set
标签
试验序号
测试的轴承
失效位置
0
1
轴承3
内圈
1
轴承4
滚动体
2
2
轴承1
外圈
3
轴承2
—
4
3
轴承1
—
5
轴承3
外圈
6
轴承4
—
Table 11Data labels and their fault types of experiment 3
Fig.8 Samples generated by different models on IMS data set
模型
类别
0
1
2
3
4
5
6
ACWGAN-GP
255.29
148.21
515.00
195.62
173.14
237.86
106.51
MDACGAN
120.00
23.40
337.67
141.61
88.01
90.92
77.35
Table 12DFI values between real samples and generated samples of IMS data set
模型
类别
0
1
2
3
4
5
6
ACWGAN-GP
0.366 8
0.323 6
0.321 6
0.344 2
0.311 2
0.332 4
0.324 5
MDACGAN
0.180 5
0.141 5
0.213 3
0.134 2
0.144 3
0.197 0
0.116 3
Table 13DMM values between real samples and generated samples of IMS data set
Fig.9 Generated samples and training samples t-SNE visualization results of IMS data set
模型
准确率/%
数据集1
数据集2
数据集3
数据集4
数据集5
数据集6
数据集7
ACWGAN-GP
75.28
86.85
88.71
91.00
89.01
91.85
97.28
MDACGAN
75.28
86.71
88.85
92.71
93.14
95.57
97.28
Table14Classification result of IMS data set
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