基于领域自适应的变工况轴承故障诊断

曹洁; 尹浩楠; 雷晓刚; 王进花

doi:10.13700/j.bh.1001-5965.2022.0631

基于领域自适应的变工况轴承故障诊断

doi: 10.13700/j.bh.1001-5965.2022.0631

曹洁^{1, 2, 3},
尹浩楠¹,
雷晓刚¹,
王进花^{1, 2, 3, ,}

1.
兰州理工大学电气工程与信息工程学院，兰州 730050
2.
甘肃省工业过程先进控制重点实验室，兰州 730050
3.
甘肃省制造信息工程研究中心，兰州 730050

基金项目: 国家重点研发计划(2020YFB1713600)；国家自然科学基金(62063020)；甘肃省自然科学基金(20JR5RA463)

详细信息

通讯作者:
E-mail：wjh0615@lut.edu.cn

中图分类号: TP277
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- 被引次数: 0
出版历程
- 收稿日期: 2022-07-19
- 录用日期: 2022-09-11
- 网络出版日期: 2022-09-27
- 整期出版日期: 2024-08-28

Bearing fault diagnosis in variable working conditions based on domain adaptation

CAO Jie^{1, 2, 3},
YIN Haonan¹,
LEI Xiaogang¹,
WANG Jinhua^{1, 2, 3
, ,}

1.
College of Electrical and Information Engineering，Lanzhou University of Technology，Lanzhou 730050，China
2.
Key Laboratory of Gansu Advanced Control of Industrial Processes，Lanzhou 730050，China
3.
Gansu Manufacturing Information Engineering Research Center，Lanzhou 730050，China

Funds: National Key Research and Development Program of China (2020YFB1713600); National Natural Science Foundation of China (62063020); Natural Science Foundation of Gansu Province (20JR5RA463)

More Information

Corresponding author: E-mail：wjh0615@lut.edu.cn

摘要

摘要:
针对轴承故障诊断中存在训练样本和测试样本分布不同及各类故障数据不平衡导致故障识别率低的问题，设计了一种基于改进残差网络(ResNet)的领域自适应故障诊断方法。在诊断网络第1层使用多维度卷积结构进行特征提取，得到不同维度的故障特征信息；在领域自适应层采用局部最大平均差异(LMMD)对齐源域和目标域的分布，获取更多细粒度信息；使用类平衡损失函数(CBLoss)解决不平衡数据的训练问题，以Adam优化网络实现故障诊断。实验结果表明，所提方法可在故障样本类别不平衡下有较高的诊断结果。在2个轴承数据集和采集的风力发电机数据上进行实验验证，结果表明，所提方法具有一定的优越性，在数据样本不平衡情况下，诊断性能优于深度神经网络和领域自适应网络等深度迁移学习方法，可作为一种有效的跨工况故障分析方法。
- 故障诊断 /
- 残差网络 /
- 数据不平衡 /
- 局部最大平均差异 /
- 类平衡损失函数 /
- 轴承
Abstract:
This research develops a domain adaptive fault diagnosis method based on an enhanced residual network (ResNet) to address the issues that the distribution of training samples and test samples differs in bearing fault diagnosis and the imbalance of different fault data results in a low fault recognition rate.First, the multi-dimensional convolution structure is used for feature extraction in the first layer of the diagnosis network to obtain fault feature information of different dimensions.Then, the local maximum mean difference (LMMD) is used in the domain adaptation layer to align the distribution of the source and target domains, to obtain more fine-grained information. Finally, the class-balanced loss (CBLoss) function is used to solve the training problem of unbalanced data, and the Adam optimization network is used to achieve fault diagnosis. The experimental findings demonstrate that, even in cases when fault sample categories are unbalanced, the enhanced approach suggested in this work can produce better diagnosis outcomes. Experiments are carried out on two bearing datasets and collected wind turbine data. The results show that the improved method has certain advantages, and its diagnostic performance is better than other deep transfer learning methods in the case of imbalanced data samples. It can be used as an effective cross-condition failure analysis method.
- fault diagnosis /
- residual network /
- data imbalance /
- local maximum mean difference /
- class-balanced loss /
- bearings

HTML全文

图 1 领域自适应图解

Figure 1. Domain adaptation diagram

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图 2 残差块结构

Figure 2. Residual block structure

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图 3 多维特征提取模块

Figure 3. Multidimensional feature extraction module

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图 4 子领域故障诊断模型

Figure 4. Sub-domain fault diagnosis model

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图 5 整体故障诊断流程

Figure 5. Overall fault diagnosis process

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图 6 美国凯斯西储大学轴承实验台

Figure 6. Bearing test bench of American Case Western Reserve University

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图 7 2种损失函数混淆矩阵

Figure 7. Confusion matrix of two loss functions

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图 8 帕德伯恩轴承实验台

1. 电机；2. 测转矩轴；3. 轴承测试模块；4. 飞轮；5. 负载电机。

Figure 8. Paderborn bearing test bench

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图 9 不同方法准确率对比

Figure 9. Accuracy comparison of different methods

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表 1 残差块参数

Table 1. Residual block parameters

残差块	残差块参数
残差块	Conv1	Conv2
Block1×2	Conv(3,3,1,16)	Conv(3,3,1,32)
Block2×2	Conv(3,3,1,32)	Conv(3,3,1,64)
Block3×2	Conv(3,3,1,64)	Conv(3,3,1,128)

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表 2 数据集设置

Table 2. Dataset settings

故障类型	尺寸/cm	训练集数量	测试集数量
正常		200	150
滚子故障	2.54	150	100
	5.08	150	100
	7.62	150	100
内圈故障	2.54	150	100
	5.08	150	100
	7.62	100	50
外圈故障	2.54	100	50
	5.08	100	50
	7.62	100	50

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表 3 不同方法诊断精度

Table 3. Diagnostic accuracy of different methods

方法	准确率/%												平均准确率
方法	A→B	A→C	A→D	B→A	B→C	B→D	C→A	C→B	C→D	D→A	D→B	D→C	平均准确率
JDA^[27]	71.3	68.5	70.2	66.8	69.9	73.6	70.4	72.6	71.2	69.1	68.5	73.6	70.5
LeNet-5	82.1	83.9	79.7	81.5	80.3	84.4	80.3	82.1	81.9	77.6	81.4	83.2	81.5
D-CORAL^[28]	98.4	96.8	88.9	96.5	97.3	95.6	91.3	97.2	98.7	87.6	93.6	95.7	94.8
DaNN^[29]	97.0	97.5	94.4	96.5	98.2	98.6	95.2	97.6	98.1	89.1	96.2	97.2	96.3
本文方法	99.4	98.8	99.1	99.3	98.5	99.8	98.2	99.1	99.5	98.4	99.7	99.3	99.1

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表 4 不同工况详细信息

Table 4. Details of different working conditions

编号	转速/（r·min⁻¹）	扭矩/（N·m）	径向负载/N
0	1500	0.7	1000
1	900	0.7	1000
2	1500	0.1	1000
3	1500	0.7	400

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表 5 目标域数据代码

Table 5. Target domain data codes

迁移任务	正常	内圈故障(OR)	外圈故障(IR)
0-1	K002	KI04	KA04
0-2	K003	KI14	KA15
0-3	K004	KI16	KA22

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表 6 轴承数据集划分

Table 6. Bearing dataset partitioning

故障类型	训练集数量	测试集数量
正常	500	300
内圈损伤	200	100
外圈损伤	100	50

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表 7 目标域数据集划分

Table 7. Target domain dataset partitioning

迁移任务	故障类型
a-b	故障Ⅰ	故障Ⅱ	故障Ⅲ
a-c	故障Ⅰ	故障Ⅱ	故障Ⅲ

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表 8 风机数据集划分

Table 8. Wind turbine dataset partitioning

故障类型	训练集数量	测试集数量
故障Ⅰ	200	100
故障Ⅱ	100	50
故障Ⅲ	50	25

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表 9 风机数据不同方法诊断精度

Table 9. Diagnostic accuracy of wind turbine data of different methods

方法	准确率/%
方法	a-b	a-c
JDA^[27]	70.7	65.6
LeNet-5	79.4	81.3
D-CORAL^[28]	96.9	95.7
DaNN^[29]	95.8	96.3
本文方法	98.2	98.4

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