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A Fault Diagnosis Approach Based on Deep Belief Network and Its Application in Bearing Fault

  • Qiulin Dan
  • Xuyu Liu
  • Yi Chai
  • Ke Zhang
  • Hao Li
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 528)

Abstract

With the development of Industry 4.0, not only the equipment but also the operational conditions in industrial manufacturing are becoming more and more complex. It is necessary to diagnose failures, whose probability is now increasing violently. As a typical deep learning model, the Deep Belief Network (DBN) can be employed to extract features from the original data directly. Compared with traditional fault diagnosis methods, the DBN can get rid of the dependence on signal processing technology and diagnosis experience. In this paper, the fault diagnosis approach based on DBN is studied to identify the bearing failure. First of all, the basic principles of DBN and the steps of fault diagnosis are described. Then some key parameters of DBN which affect the fault identification performance are analyzed and determined according to the simulation experiments. The practicability of this method is verified by comparing with Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN) at last.

Keywords

Signal processing Fault diagnosis Deep Belief Network Feature extraction 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Qiulin Dan
    • 1
    • 2
  • Xuyu Liu
    • 1
  • Yi Chai
    • 1
    • 2
  • Ke Zhang
    • 1
    • 2
  • Hao Li
    • 1
    • 2
  1. 1.School of AutomationChongqing UniversityChongqing CityChina
  2. 2.Key Laboratory of Complex System Safety and ControlMinistry of Education, Chongqing UniversityChongqing CityChina

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