Gear Fault Diagnosis Based on Deep Learning and Subdomain Adaptation

被引：0

作者：

Jie Z. ^{[1
]}

Wang X. ^{[2
]}

Gong T. ^{[2
]}

机构：

[1] School of Aircraft Engineering, Nanchang Hangkong University, Nanchang

[2] School of Navigation, Nanchang Hangkong University, Nanchang

来源：

Zhongguo Jixie Gongcheng/China Mechanical Engineering | 2021年 / 32卷 / 22期

关键词：

Convolutional neural network(CNN); Gear fault diagnosis; Local maximum mean discrepancy; Subdomain adaptation;

D O I：

10.3969/j.issn.1004-132X.2021.22.008

中图分类号：

学科分类号：

摘要：

Aiming at the insufficient labeled fault data in real cases, a method was proposed based on deep learning and subdomain adaptation. The domain-shared one dimensional CNN was first used to extract transferable features from the fault data. Then, the multi-kernel local maximum mean discrepancy was used to measure the distribution discrepancy of the learned transferable features relevant subdomains, and the measured distribution discrepancy was added to the objective function for training. Finally, the trained model was used to identify the health conditions of the target domain. The results show that the proposed method may achieve high accuracy in the case of target domain data without label. © 2021, China Mechanical Engineering Magazine Office. All right reserved.

引用

页码：2716 / 2723

页数：7

共 19 条

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