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作者:

Jiao, Zhaonan (Jiao, Zhaonan.) | Zhang, Xiliang (Zhang, Xiliang.) | Sun, Yuqi (Sun, Yuqi.) | Hua, Xia (Hua, Xia.) | Li, Huayu (Li, Huayu.)

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摘要:

This paper proposes a method to diagnose machine faults by introducing image processing technique. In this paper, the machine fault diagnosis is considered as a problem of Black Casket. Inputs of the Black Casket are voltage excitation signals, and outputs of the Black Casket are current responses signals. In order to deal with the problem of Black Casket, a health-condition image is built based on voltage excitation and current responses of the machine. Thus, machine faults are shown in forms of health-condition image deformations. An image feature detection algorithm, Maximally Stable Extremal Region (MSER), is used to detect image deformation of the health-condition image. The open-phase fault of the machine is studied as an example of machine faults. The effectiveness of the proposed method is verified by simulation results. © 2022 Division of Signal Processing and Electronic Systems, Poznan University of Technology (DSPES PUT).

关键词:

Image processing Deformation Permanent magnets Failure analysis Health Fault detection

作者机构:

  • [ 1 ] [Jiao, Zhaonan]Beijing University of Technology, Faculty of Information, Beijing; 100124, China
  • [ 2 ] [Zhang, Xiliang]Beijing University of Technology, Faculty of Information, Beijing; 100124, China
  • [ 3 ] [Sun, Yuqi]Beijing University of Technology, Faculty of Information, Beijing; 100124, China
  • [ 4 ] [Hua, Xia]Beijing University of Technology, Faculty of Information, Beijing; 100124, China
  • [ 5 ] [Li, Huayu]Beijing University of Technology, Faculty of Information, Beijing; 100124, China

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ISSN: 2326-0262

年份: 2022

卷: 2022-September

页码: 76-80

语种: 英文

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