Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (12): 3626-3631.DOI: 10.11772/j.issn.1001-9081.2021060974
• The 18th China Conference on Machine Learning • Previous Articles
Xueying PENG, Yongquan JIANG(), Yan YANG
Received:
2021-05-12
Revised:
2021-06-13
Accepted:
2021-06-29
Online:
2021-12-28
Published:
2021-12-10
Contact:
Yongquan JIANG
About author:
PENG Xueying, born in 1996, M. S. candidate. Her research interests include deep learning.Supported by:
通讯作者:
江永全
作者简介:
彭雪莹(1996—),女,四川成都人,硕士研究生,主要研究方向:深度学习基金资助:
CLC Number:
Xueying PENG, Yongquan JIANG, Yan YANG. Transfer learning based on graph convolutional network in bearing service fault diagnosis[J]. Journal of Computer Applications, 2021, 41(12): 3626-3631.
彭雪莹, 江永全, 杨燕. 基于图卷积网络的迁移学习轴承服役故障诊断[J]. 《计算机应用》唯一官方网站, 2021, 41(12): 3626-3631.
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URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021060974
任务 | 源域 | 目标域 |
---|---|---|
1 | PU | PU |
2 | IMS | |
3 | PHM | |
4 | CWRU | PU |
5 | IMS | |
6 | PHM |
Tab. 1 Transfer task from simulated faults to service faults
任务 | 源域 | 目标域 |
---|---|---|
1 | PU | PU |
2 | IMS | |
3 | PHM | |
4 | CWRU | PU |
5 | IMS | |
6 | PHM |
任务 | Non-transfer | MMD | GCNTL |
---|---|---|---|
1 | 33.94 | 82.81 | 83.14 |
2 | 25.12 | 81.68 | 82.37 |
3 | 22.38 | 81.53 | 82.42 |
4 | 26.19 | 81.04 | 81.27 |
5 | 19.88 | 81.15 | 81.09 |
6 | 17.08 | 80.51 | 80.84 |
Tab. 2 Diagnosis accuracy of model on bearing service fault unit: %
任务 | Non-transfer | MMD | GCNTL |
---|---|---|---|
1 | 33.94 | 82.81 | 83.14 |
2 | 25.12 | 81.68 | 82.37 |
3 | 22.38 | 81.53 | 82.42 |
4 | 26.19 | 81.04 | 81.27 |
5 | 19.88 | 81.15 | 81.09 |
6 | 17.08 | 80.51 | 80.84 |
任务 | 源域 | 目标域 | ||||
---|---|---|---|---|---|---|
旋转速度 /(r·min-1) | 负载扭矩/(N·m) | 径向力/N | 旋转速度 /(r·min-1) | 负载扭矩/(N·m) | 径向力/N | |
1 | 1 500 | 0.7 | 1 000 | 1 500 | 0.7 | 400 |
2 | 1 500 | 0.1 | 1 000 | |||
3 | 900 | 0.7 | 1 000 |
Tab. 3 Transfer task on PU bearing dataset
任务 | 源域 | 目标域 | ||||
---|---|---|---|---|---|---|
旋转速度 /(r·min-1) | 负载扭矩/(N·m) | 径向力/N | 旋转速度 /(r·min-1) | 负载扭矩/(N·m) | 径向力/N | |
1 | 1 500 | 0.7 | 1 000 | 1 500 | 0.7 | 400 |
2 | 1 500 | 0.1 | 1 000 | |||
3 | 900 | 0.7 | 1 000 |
任务 | 源域 | 目标域 | ||||
---|---|---|---|---|---|---|
采样位置 | 采样频率 /kHz | 电机负载/hp | 采样位置 | 采样频率 /kHz | 电机负载/hp | |
4 | 驱动端 | 12 | 0 | 驱动端 | 12 | 1 |
5 | 驱动端 | 48 | 0 | |||
6 | 风扇端 | 12 | 0 |
Tab. 4 Transfer task on CWRU bearing dataset
任务 | 源域 | 目标域 | ||||
---|---|---|---|---|---|---|
采样位置 | 采样频率 /kHz | 电机负载/hp | 采样位置 | 采样频率 /kHz | 电机负载/hp | |
4 | 驱动端 | 12 | 0 | 驱动端 | 12 | 1 |
5 | 驱动端 | 48 | 0 | |||
6 | 风扇端 | 12 | 0 |
任务 | 准确率 | 任务 | 准确率 |
---|---|---|---|
1 | 88.29 | 4 | 94.18 |
2 | 88.06 | 5 | 94.30 |
3 | 88.21 | 6 | 93.94 |
Tab. 5 Diagnosis accuracy of model transfer between different working conditions
任务 | 准确率 | 任务 | 准确率 |
---|---|---|---|
1 | 88.29 | 4 | 94.18 |
2 | 88.06 | 5 | 94.30 |
3 | 88.21 | 6 | 93.94 |
任务 | TCA | DAFD | CNN-fine-tune | DANN | DCTLN | FTNN | GCNTL |
---|---|---|---|---|---|---|---|
均值 | 40.94 | 54.48 | 77.16 | 80.02 | 81.67 | 81.16 | 81.85 |
1 | 43.82 | 56.72 | 80.58 | 81.37 | 82.81 | 82.63 | 83.16 |
2 | 39.36 | 53.24 | 76.61 | 79.88 | 81.97 | 81.59 | 82.36 |
3 | 39.51 | 53.96 | 75.87 | 79.17 | 82.40 | 81.22 | 82.42 |
4 | 41.86 | 55.01 | 77.49 | 80.48 | 81.27 | 81.15 | 81.27 |
5 | 40.67 | 54.93 | 76.25 | 79.31 | 81.83 | 80.29 | 81.12 |
6 | 40.44 | 53.04 | 76.18 | 79.91 | 79.73 | 80.07 | 80.78 |
Tab. 6 Diagnosis accuracies of different models on 6 tasks
任务 | TCA | DAFD | CNN-fine-tune | DANN | DCTLN | FTNN | GCNTL |
---|---|---|---|---|---|---|---|
均值 | 40.94 | 54.48 | 77.16 | 80.02 | 81.67 | 81.16 | 81.85 |
1 | 43.82 | 56.72 | 80.58 | 81.37 | 82.81 | 82.63 | 83.16 |
2 | 39.36 | 53.24 | 76.61 | 79.88 | 81.97 | 81.59 | 82.36 |
3 | 39.51 | 53.96 | 75.87 | 79.17 | 82.40 | 81.22 | 82.42 |
4 | 41.86 | 55.01 | 77.49 | 80.48 | 81.27 | 81.15 | 81.27 |
5 | 40.67 | 54.93 | 76.25 | 79.31 | 81.83 | 80.29 | 81.12 |
6 | 40.44 | 53.04 | 76.18 | 79.91 | 79.73 | 80.07 | 80.78 |
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