Deep learning theory and its application to fault diagnosis of an electric machine
DOI:10.19783/j.cnki.pspc.190712
Key Words:electric machine  fault diagnosis  deep learning  deep belief network  auto-encoders  convolutional neural networks  recurrent neural network
Author NameAffiliation
DING Shichuan School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China
National Engineering Laboratory of Energy-Saving Motor & Control Technology, Anhui University, Hefei 230601, China 
LI Xueyi School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China
National Engineering Laboratory of Energy-Saving Motor & Control Technology, Anhui University, Hefei 230601, China 
HANG Jun School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China
National Engineering Laboratory of Energy-Saving Motor & Control Technology, Anhui University, Hefei 230601, China 
WANG Yinjiang School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China
National Engineering Laboratory of Energy-Saving Motor & Control Technology, Anhui University, Hefei 230601, China 
WANG Qunjing School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China
National Engineering Laboratory of Energy-Saving Motor & Control Technology, Anhui University, Hefei 230601, China 
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Abstract:The electric machine has been widely used in various fields, and its failure may not only cause damage to the machine, but also many problems, such as economic loss, casualties and so on. Therefore, it is important to apply timely and efficient fault diagnosis technology. Deep learning has been applied in fault diagnosis of electric machines and obtained some useful results because of its more powerful and more complex feature expression ability than traditional techniques. Hence, this paper introduces four classic types of deep learning model, the Deep Belief Networks (DBN), Auto-Encoders (AE), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), and summarizes the application of these four deep learning models in electric machine fault diagnosis. Finally, the problems and challenges that deep learning faces in this application are summarized and prospects discussed. This work is supported by National Natural Science Foundation of China (No. 51637001, No. 51607001, and No. 51507002) and Natural Science Foundation of Anhui Province (No. 1508085ME87 and No. 1708085QE108).
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