Method to enhance deep learning fault diagnosis by generating adversarial samples
Cao, Jie1,4; Ma, Jialin1; Huang, Dailin1; Yu, Ping2; Wang, Jinhua2; Zheng, Kangjie3
2022-02
发表期刊Applied Soft Computing
ISSN1568-4946
卷号116
摘要Modern industrial fields utilize complex mechanical equipment and machinery, which are closely linked, and equipment faults are difficult to express. Therefore, fault diagnosis is important to ensure the safety of complex mechanical equipment in modern industries. Deep learning has achieved excellent results with recent fault diagnosis methods. At present, three common deep learning models (MLP, CNN, and RNN models) can achieve diagnosis rates close to 100% with original fault diagnosis data and a signal-to-noise ratio above 10 dB. However, we found that the diagnostic rate of these three models was completely incorrect when an adversarial sample with a signal-to-noise ratio noise greater than 10 dB was added to the original sample. We propose a GAN-based adversarial signal generative adversarial network (AdvSGAN) in this paper. We conduct experiments on the CWRU dataset and conclude that we can easily obtain adversarial noise and generate training samples through AdvSGAN. With the addition of adversarial data training, the diagnostic rate of the model on these adversarial samples increased from less than 5% to 98.69%, 97.38% and 96.94%. Hence, this method increases the reliability of our deep learning model. © 2021 Elsevier B.V.
关键词Accident prevention Complex networks Convolutional neural networks Fault detection Generative adversarial networks Machinery Recurrent neural networks Signal to noise ratio Adversarial-signal generative adversarial network Convolutional neural network Fault diagnosis method Faults diagnosis Industrial fields Learning models Mechanical equipment Multilayers perceptrons Original sample Three models
DOI10.1016/j.asoc.2021.108385
收录类别EI ; SCIE
语种英语
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications
WOS记录号WOS:000768204300009
出版者Elsevier Ltd
EI入藏号20220211449083
EI主题词Failure analysis
EI分类号716.1 Information Theory and Signal Processing ; 722 Computer Systems and Equipment ; 723.4 Artificial Intelligence ; 914.1 Accidents and Accident Prevention
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被引频次:7[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符https://ir.lut.edu.cn/handle/2XXMBERH/157945
专题电气工程与信息工程学院
通讯作者Ma, Jialin
作者单位1.Lanzhou Univ Technol, Coll Comp & Commun, Lanzhou 730050, Peoples R China;
2.Lanzhou Univ Technol, Coll Elect & Informat Engn, Lanzhou 730050, Peoples R China;
3.Dalian Maritime Univ, Informat Sci & Engn, Dalian 116000, Peoples R China;
4.Engn Res Ctr Mfg Informat Gansu Prov, Lanzhou 287, Peoples R China
第一作者单位计算机与通信学院
通讯作者单位计算机与通信学院
第一作者的第一单位计算机与通信学院
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Cao, Jie,Ma, Jialin,Huang, Dailin,et al. Method to enhance deep learning fault diagnosis by generating adversarial samples[J]. Applied Soft Computing,2022,116.
APA Cao, Jie,Ma, Jialin,Huang, Dailin,Yu, Ping,Wang, Jinhua,&Zheng, Kangjie.(2022).Method to enhance deep learning fault diagnosis by generating adversarial samples.Applied Soft Computing,116.
MLA Cao, Jie,et al."Method to enhance deep learning fault diagnosis by generating adversarial samples".Applied Soft Computing 116(2022).
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