Bearing Fault Diagnosis Based on SA-ACGAN Data Generation Model

被引：0

作者：

Yang G. ^{[1
,2
]}

Liu L. ^{[1
]}

Xi C. ^{[1
]}

机构：

[1] Institute of Agricultural Machinery, Hubei University of Technology, Wuhan

[2] Hubei Engineering Research Center for Intellectualization of Agricultural Equipment, Wuhan

来源：

Zhongguo Jixie Gongcheng/China Mechanical Engineering | 2022年 / 33卷 / 13期

关键词：

deep learning; fault diagnosis; generative adversarial network（GAN）; rolling bear;

D O I：

10.3969/j.issn.1004-132X.2022.13.012

中图分类号：

学科分类号：

摘要：

Unbalancing training dataset caused by the difficulty in obtaining fault samples seriously affectsed the robust and accuracy of fault diagnosis model. A data generation model was proposed based on self-adaptive auxiliary classifier GAN, which adaptively adjusted the generator loss by measuring the relative performance between discriminator and generator, accelerated the converge speed of training processes, and improved the quality of generated data. The raw data, data generated by auxiliary classifier GAN method, and data generated by proposed method were used as the input data of the BP neural network. The results show that the model trained by data of the proposed method was superior. Comparison results of the proposed method with 1D-CNN,e2e-LSTM,CFVS-SVM, and FFT-CNN fault diagnosis methods manifest that the proposed method is better in fault diagnosis accuracy and data processing time. © 2022 China Mechanical Engineering Magazine Office. All rights reserved.

引用

页码：1613 / 1621

页数：8

共 14 条

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