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Fault Diagnosis of Motor in Frequency Domain Signal by stacked de-noising auto-encoder

Computers, Materials & Continua 2018年第11期57卷 223-242页

作者： Xiaoping Zhao Jiaxin Wu Yonghong Zhang Yunqing Shi Lihua Wang School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China School of Information and Control Nanjing University of Information Science and TechnologyNanjing210044China Electrical and Computer Engineering New Jersey Institute of TechnologyNewark07102USA

With the rapid development of mechanical equipment,mechanical health monitoring field has entered the era of big *** 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 ***,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 *** frequency domain signals obtained by the Fourier transform are used as input to the *** 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 *** 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.

关键词： Big data deep learning stacked de-noising auto-encoder fourier transform

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