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Fault Diagnosis of Motor in Frequency Domain Signal by Stacked De-noising Auto-encoder

Xiaoping Zhao1, Jiaxin Wu1,*, Yonghong Zhang2, Yunqing Shi3, Lihua Wang2
School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
School of Information and Control, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, 07102, USA.
* Corresponding Author: Jiaxin Wu. Email: .

Computers, Materials & Continua 2018, 57(2), 223-242.


With the rapid development of mechanical equipment, mechanical health monitoring field has entered the era of big data. Deep learning has made a great achievement in the processing of large data of image and speech due to the powerful modeling capabilities, this also brings influence to the mechanical fault diagnosis field. Therefore, according to the characteristics of motor vibration signals (nonstationary and difficult to deal with) and mechanical ‘big data’, combined with deep learning, a motor fault diagnosis method based on stacked de-noising auto-encoder is proposed. The frequency domain signals obtained by the Fourier transform are used as input to the network. This method can extract features adaptively and unsupervised, and get rid of the dependence of traditional machine learning methods on human extraction features. A supervised fine tuning of the model is then carried out by backpropagation. The Asynchronous motor in Drivetrain Dynamics Simulator system was taken as the research object, the effectiveness of the proposed method was verified by a large number of data, and research on visualization of network output, the results shown that the SDAE method is more efficient and more intelligent.


Big data, deep learning, stacked de-noising auto-encoder, fourier transform.

Cite This Article

X. Zhao, J. Wu, Y. Zhang, Y. Shi and L. Wang, "Fault diagnosis of motor in frequency domain signal by stacked de-noising auto-encoder," Computers, Materials & Continua, vol. 57, no.2, pp. 223–242, 2018.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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