子域适应无监督轴承故障诊断

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振动与冲击 ›› 2021, Vol. 40 ›› Issue (15) : 34-40.

论文

子域适应无监督轴承故障诊断

吴静然1，2，刘建华2，崔冉1，2

作者信息 +

Sub-domain adaptive unsupervised bearing fault diagnosis

WU Jingran1,2, LIU Jianhua2, CUI Ran1,2

Author information +

文章历史 +

摘要

针对深度无监督轴承故障诊断网络仅对齐全局分布，未考虑源域和目标域每个类别细粒度信息的问题，提出了一种子域适应无监督端到端轴承故障诊断网络。该网络采用一维卷积神经网络进行特征提取，利用多分类函数构建分类器，通过最小化局部最大平均差异和分类器损失函数，进行相关子域的分布对齐。在江南大学轴承故障数据集对该方法进行有效性验证。结果表明，该方法在目标域数据无标签的情况下，识别正确率明显高于其他5种目前流行的领域自适应故障诊断方法，t分布随机邻居嵌入结果显示该方法有效对齐源域和目标域类别信息，验证了该方法的可行性和有效性。

Abstract

Here, aiming at the problem of deep unsupervised bearing fault diagnosis network only aligning global distribution without considering the fine-grained information of each category in source domain and target domain, a sub-domain adaptive unsupervised end-to-end bearing fault diagnosis network was proposed. Firstly, 1-D convolutional neural network was used to do feature extraction, and multi-classification functions were used to construct classifiers. Then, by minimizing the local maximum average difference and loss function of classifier, the distribution of related sub-domains was aligned. Finally, the effectiveness of the proposed method was verified with the bearing fault data set of Jiangnan University. The results showed that the recognition accuracy of the proposed method is obviously higher than those of the other 5 popular domain adaptive fault diagnosis methods when the target domain data is unlabeled. The t-distributed random neighbor embedding results showed that the proposed method can effectively align category information of source domain and target domain; the feasibility and effectiveness of the proposed method are verified.

导出引用

吴静然，刘建华，崔冉1. 子域适应无监督轴承故障诊断[J]. 振动与冲击, 2021, 40(15): 34-40

WU Jingran, LIU Jianhua, CUI Ran. Sub-domain adaptive unsupervised bearing fault diagnosis[J]. Journal of Vibration and Shock, 2021, 40(15): 34-40

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