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Volume 49 Issue 8
Aug.  2023
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Journal of Beijing University of Aeronautics and Astronautics  > 2023  >  49(8): 1896-1903.
WANG J H，GAO Y，CAO J，et al. Fault diagnosis of generator rolling bearing based on AE-BN[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（8）：1896-1903 （in Chinese） doi: 10.13700/j.bh.1001-5965.2021.0581
Citation: WANG J H，GAO Y，CAO J，et al. Fault diagnosis of generator rolling bearing based on AE-BN[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（8）：1896-1903 （in Chinese） doi: 10.13700/j.bh.1001-5965.2021.0581
shu

Fault diagnosis of generator rolling bearing based on AE-BN

doi: 10.13700/j.bh.1001-5965.2021.0581
  • 1.

    College of Electrical & Information Engineering，Lanzhou University of Technology，Lanzhou 730050，China

  • 2.

    Key Laboratory of Gansu Advanced Control for Industrial Processes，Lanzhou University of Technology，Lanzhou 730050，China

  • 3.

    National Experimental Teaching Center of Electrical and Control Engineering，Lanzhou University of Technology，Lanzhou 730050，China

  • 4.

    Engineering Research Center of Manufacturing Information of Gansu Province，Lanzhou 730050，China

  • 5.

    College of Computer and Communication，Lanzhou University of Technology，Lanzhou 730050，China

Funds:

National Natural Science Foundation of China (62063020,61763028); Natural Science Foundation of Gansu Province, China (20JR5RA463) 

More Information
  • Corresponding author: E-mail：wjh0615@lut.edu.cn
  • Received Date: 30 Sep 2021
  • Accepted Date: 27 Dec 2021
  • Publish Date: 26 Jan 2022
    Abstract

  • To solve the accuracy of fault identification of wind turbines under complex working conditions, coupling, and uncertainty, an AE-BN fault diagnosis method based on a auto-encoder (AE) and Bayesian network (BN) is proposed. AE is used to extract the characteristics of the current signal, and the characteristic component that can highly characterize the signal is obtained; based on the causal relationship between fault and feature, a three-layer BN composed of fault location, fault state, and fault feature is established; The wind turbine fault diagnostic model is then established, the uncertainty problem in fault diagnosis is solved, and the precision of multi fault diagnosis is enhanced by combining the characteristic component of AE with the topology of BN. Experimental results show that the proposed method can analyze and diagnose fault characteristic signals and accurately identify different fault types, which has obvious advantages over other algorithms.

     

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