Bearing Fault Diagnosis Method Based on Small Sample Data under Unbalanced Loads

被引：5

作者：

He Q. ^{[1
]}

Tang X. ^{[1
,2
,3
]}

Li C. ^{[1
,2
,3
]}

Lu J. ^{[1
,2
,3
]}

Chen J. ^{[1
]}

机构：

[1] Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang

[2] School of Mechanical Engineering, Guizhou University, Guiyang

[3] State Key Laboratory of Public Big Data, Guizhou University, Guiyang

来源：

Zhongguo Jixie Gongcheng/China Mechanical Engineering | 2021年 / 32卷 / 10期

关键词：

Bearing fault diagnosis; Convolutional neural network with self-attention mechanism(SeCNN); Gradient penalty Wasserstein generative adversarial network(WGAN-GP); Short-time Fourier transform; Small sample; Unbalanced load;

D O I：

10.3969/j.issn.1004-132X.2021.10.004

中图分类号：

学科分类号：

摘要：

Aiming at the problems that the bearing vibration signals were easily disturbed by unbalanced load and the small number of bearing fault samples, a bearing fault diagnosis method based on WGAN-GP and SeCNN was proposed. The bearing vibration signals were processed by short-time Fourier transform to get the time-spectrum samples that were easy to be processed by WGAN-GP, which were divided into training set, validation set and test set. Then the training set was inputted into WGAN-GP for adversarial training, new samples were generated with similar distribution to the training samples, and added to the training set to expand the training set. The expanded training set was input into SeCNN for learning, and the trained model was applied to the test set and output the fault recognition results. The analysis of the CUT-2 platform unbalanced load bearing data was carried out, and the experimental results show that the proposed method may accurately and effectively classify the bearing faults. © 2021, China Mechanical Engineering Magazine Office. All right reserved.

引用

页码：1164 / 1171and1180

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