Fault diagnosis of motor in frequency domain signal by stacked de-noising auto-encoder

Xiaoping Zhao, Jiaxin Wu, Yonghong Zhang, Yunqing Shi, Lihua Wang

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)223-242
Number of pages20
JournalComputers, Materials and Continua
Issue number2
StatePublished - 2018

All Science Journal Classification (ASJC) codes

  • Biomaterials
  • Modeling and Simulation
  • Mechanics of Materials
  • Computer Science Applications
  • Electrical and Electronic Engineering


  • Big data
  • Deep learning
  • Fourier transform
  • Stacked de-noising auto-encoder


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