类别标签辅助改进稠密网络的变工况轴承故障诊断

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摘要基于数据驱动的滚动轴承智能故障诊断得到广泛研究，但多数研究中均假设训练数据与测试数据同分布，考虑到旋转机械实际运转中复杂多变的工况往往导致数据分布产生偏差，使得识别方法的通用性差、实际识别效果不佳。本文将域适应引入轴承故障诊断过程中，基于迁移学习提出了一种特征空间域和标签概率分布同步适应的迁移学习网络。该网络将一维稠密卷积网络及注意力机制融合实现复杂故障特征的自动提取；域适应处理通过联合最小化特征概率分布差异和标签概率分布差异来约束网络学习域不变特征；最终对变工况滚动轴承故障实现了高准确度的识别。实验结果表明了该方法的可行性及良好的性能。
关键词：轴承故障诊断；变工况；稠密卷积网络；注意力机制；类别标签辅助

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	孙洁娣1
	2
	刘保1
	温江涛3
	时培明3
	闫盛楠1
	2
	肖启阳4

关键词 ：轴承故障诊断, 变工况, 稠密卷积网络, 注意力机制, 类别标签辅助

Abstract：Intelligent fault diagnosis-based data-driven for rolling bearings has been widely studied, however, most researches assume that training data and test data are the same distribution. The complex and variable working conditions in the actual operation of rotating machinery often lead to deviations in data distribution, which result in poor versatility and low identification accuracy. This paper introduced domain adaptation and proposed a transfer diagnosis network with synchronous adaptation of feature space domain and label probability distribution. The network combines a one-dimensional dense convolutional network and attention mechanism to automatically extract complex fault features; the domain adaptation constrains the network to learn domain invariant features by jointly minimizing the difference in feature probability distribution and label probability distribution; finally rolling bearing faults under variable operating conditions can be identified with high accuracy. The experimental results show the validity and better performance of this method.
Key words: bearing fault diagnosis; variable working conditions; dense convolution network; attention mechanism; label auxiliary

Key words： bearing fault diagnosis variable working conditions dense convolution network attention mechanism label auxiliary

收稿日期: 2021-04-13 出版日期: 2022-09-15

引用本文:

孙洁娣1,2，刘保1，温江涛3，时培明3，闫盛楠1,2,肖启阳4. 类别标签辅助改进稠密网络的变工况轴承故障诊断[J]. 振动与冲击, 2022, 41(17): 204-212.
SUN Jiedi1,2, LIU Bao1, WEN Jiangtao3, SHI Peiming3, YAN Shengnan1,2, XIAO Qiyang4. Fault diagnosis of variable condition bearing based on improved dense network aided by class labels. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(17): 204-212.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2022/V41/I17/204

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