迁移学习和CNN的电机故障诊断方法

谢锋云; 董建坤; 符羽; 刘翊; 肖乾

doi:10.13433/j.cnki.1003-8728.20220196

迁移学习和CNN的电机故障诊断方法

doi: 10.13433/j.cnki.1003-8728.20220196

1.
华东交通大学机电与车辆工程学院，南昌　330013
2.
国家先进轨道交通装备创新中心，湖南株洲　412001

基金项目: 国家自然科学基金项目（52265068）与江西省自然科学基金项目（20224BAB204050)

详细信息

作者简介:
谢锋云，副教授，硕士生导师，xiefyun@163.com

中图分类号: TG156
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- 文章访问数: 64
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- PDF下载量: 1
- 被引次数: 0
出版历程
- 收稿日期: 2021-10-12
- 刊出日期: 2024-03-25

Motor Fault Diagnosis Method Based on Migration Learning and CNN

1.
School of Mechanical Electronical and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
2.
National Innovation Center of Advanced Rail Transit Equipment, Zhuzhou 412001, Hu′nan, China

摘要

摘要: 针对缺乏数据导致卷积神经网络（Convolutional neural networks，CNN）训练不佳的问题，以三相异步电机故障诊断为研究对象，提出了基于迁移学习和CNN结合的电机故障诊断方法。首先搭建了电机故障诊断实验平台，通过加速度传感器获取CNN模型带标签数据，通过训练获取预训练模型；然后结合迁移学习得到的预训练模型迁移到目标域，并通过对目标域的带标签数据进行训练以优化CNN参数；最终获得可以对目标域数据有着良好分类能力的新模型，从而实现目标域带标签数据稀少情况下的电机故障诊断工作。通过将该方法与传统CNN、变分模态分解（Variational modal decomposition，VMD）-支持向量机（Support vector machine，SVM）、VMD-K近邻（K nearest neighbor，KNN）以及VMD-BP神经网络等识别模型进行对比验证，结果显示本文提出的迁移CNN模型模式识别方法有更好的识别效果。
- CNN /
- 迁移学习 /
- 三相异步电机 /
- VMD /
- 故障诊断
Abstract: Aiming at the problem that the lack of labeled data will lead to poor training of convolutional neural network (CNN), a motor fault diagnosis method based on the combination of migration learning and CNN is proposed for three-phase asynchronous motor fault diagnosis. Firstly, an experimental platform for motor fault diagnosis is built, the label data of input CNN model is obtained by acceleration sensor, and the pre-trained model is obtained through training. Then, the obtained pre-training model is transferred to the target domain with transfer learning, and a small amount of labeled data in the target domain is cleared for training and fine-tuning parameters, and the CNN parameters are optimized by training the labeled data in the target domain. Finally, a new model with good classification ability for the target domain data is obtained, so as to realize the motor fault diagnosis in the case of scarce labeled data in the target domain. By comparing this method with ordinary CNN, variational modal decomposition (VMD)-support vector machine (SVM), VMD-K nearest neighbor (KNN) and VMD-BP neural network recognition models for validation, the results show that the pattern recognition method of migrating CNN model proposed in this paper has better recognition effect.
- CNN /
- transfer learning /
- three phase asynchronous motor /
- VMD /
- fault diagnosis

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图 1 CNN模型结构图

Figure 1. CNN model structure diagram

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图 2 电机迁移CNN学习流程图

Figure 2. Flow chart of CNN transfer learning for motor

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图 3 实验装置连接图

Figure 3. Connection of experimental devic

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图 4 电机故障诊断实验平台

Figure 4. Motor fault diagnosis experimental platform

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图 5 小样本情况下传统CNN的10次识别结果

Figure 5. 10 recognition results of traditional CNN under small sample size

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图 6 B工况使用传统CNN测试结果混淆矩阵

Figure 6. Confusion matrix of test results using traditional CNN in Condition B

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图 7 A→B直接使用训练模型识别结果

Figure 7. A→B recognition results using the training model

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图 8 A→B情况下10次识别结果

Figure 8. 10 recognition results in the A→B case

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图 9 A→B情况下测试结果混淆矩阵

Figure 9. Confusion matrix of test results in the A→B case

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表 1 迁移学习和传统机器学习的区别

Table 1. Differences between transfer learning and traditional machine learning

区别	迁移学习	传统机器学习
数据分布	不需要同分布	同分布
标签数据	少量标签数据	大量标签数据
建立模型	可以用训练过的模型	重新建模

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表 2 CNN模型参数

Table 2. CNN model parameters

网络层	卷积核大小	步长	滤波器数量	输出大小	参数个数
输入层	1024 × 1		64
卷积层1	16	4	64	256 × 64	1088
最大池化层1	2	2	64	128 × 64
卷积层2	3	1	64	128 × 64	12352
最大池化层2	2	2	64	64 × 64
卷积层3	3	2	128	32 × 128	24704
最大池化层3	2	2	128	16 × 128
卷积层4	3	1	256	16 × 256	98560
全局池化层	16	1	256	256
全连接层	768	1		768	197376
Softmax层		1		3	2307

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表 3 A→B迁移数据集

Table 3. A→B migration dataset

迁移任务	源域转速	目标域转速	源域样本个数	目标域样本个数
A→B	600	900	1800	1836
B→A	900	600	1800	1836
A→C	600	1200	1800	1836
C→A	1200	600	1800	1836
B→C	900	1200	1800	1836
C→B	1200	900	1800	1836

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表 4 不同冻结方法测试对比

Table 4. Comparison of tests using different freezing methods

方法	平均识别率/%	单次模型训练时间/s
不冻结	98.04	1.79
冻结卷积层1	95.82	1.71
冻结卷积层2	93.62	1.68
冻结卷积层3	91.38	1.51
冻结卷积层4	87.43	1.31
冻结全连接层	78.79	1.02
冻结Softmax层	60.17	0

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表 5 A→B情况下迁移学习模型指标

Table 5. Transfer learning model indicators in the A→B case

参数	精确率	召回率	F1值	数量
正常	0.9932	0.975	0.984	600
转子断条	0.9781	0.9933	0.9856	600
轴承故障	0.9967	1	0.9983	600

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表 6 不同方法对比

Table 6. Comparison of different methods

方法	平均总体识别率/%
方法	A→B	B→A	A→C	C→A	B→C	C→B
迁移CNN	97.39	98.43	95.92	98.38	96.71	96.73
传统CNN	90.31	86.4	81.45	86.4	81.45	90.31
VMD-SVM	83.61	89.72	84.27	89.72	84.27	83.61
VMD-KNN	86.11	82.5	78.32	82.5	78.32	86.11
VMD-BP	81.56	77.63	73.70	77.63	73.70	81.56

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