基于子域自适应对抗网络的轴承故障诊断

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振动与冲击 ›› 2022, Vol. 41 ›› Issue (11) : 114-122.

论文

基于子域自适应对抗网络的轴承故障诊断

周华锋，程培源，邵思羽，赵玉伟

作者信息 +

Bearing fault diagnosis based on subdomain adaptive confrontation network

ZHOU Huafeng, CHENG Peiyuan, SHAO Siyu, ZHAO Yuwei

Author information +

文章历史 +

摘要

现有基于深度学习网络模型的故障诊断方法往往依赖大量有标签数据进行训练，在变工况条件下，模型的诊断精度会有所下降。针对此，为提高变工况条件下的故障诊断准确率，基于域自适应理论提出一种新颖的网络模型——子域自适应对抗网络。该网络模型不仅充分利用了动态卷积的特征提取能力，同时还借鉴了生成对抗网络的博弈思想，使特征生成器和分类器对抗学习，利用每个类别的决策边界对样本进行正确分类；此外，在对抗网络中引入局部最大平均差异，考虑每个类别的细粒度信息，以此来对齐源域和目标域相应的类空间，减小网络模型在决策边界附近的分类误差，从而提高模型对故障类别的识别精度。最终，通过两个数据集对所提出的方法进行实验验证，结果表明模型在变工况条件下具有较强的泛化性能与良好的故障识别精度。

Abstract

Existing deep learning-based fault diagnosis methods mainly rely on sufficient labeled data for training, where the diagnosis accuracy may decrease under variable operating conditions. To solve this problem, based on the domain adaptation theory, this paper proposes a novel fault diagnosis framework called subdomain adaptive adversarial network (SAAN) to improve diagnosis accuracy when dealing with variable operating conditions. The proposed method not only makes full use of feature extraction capabilities of dynamic convolution, but also draws on the game idea of generative adversarial networks where feature generator and classifier compete with each other. In addition, the decision boundary of each category is utilized to contribute to accurate classification. Furthermore, the local maximum mean discrepancy (LMMD) is also introduced in the generative network, and the fine-grained information of each category is considered to align the corresponding class spaces of the source and target domains, reducing model classification error near the decision boundary. Finally, the proposed method is verified through two different data sets. Experimental results have shown that the proposed framework has strong generalization performance and desired fault diagnosis accuracy under variable operating conditions.

导出引用

周华锋，程培源，邵思羽，赵玉伟. 基于子域自适应对抗网络的轴承故障诊断[J]. 振动与冲击, 2022, 41(11): 114-122

ZHOU Huafeng, CHENG Peiyuan, SHAO Siyu, ZHAO Yuwei. Bearing fault diagnosis based on subdomain adaptive confrontation network[J]. Journal of Vibration and Shock, 2022, 41(11): 114-122

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