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Author:

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

Indexed by:

CPCI-S

Abstract:

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.

Keyword:

Image Processing Black Casket Maximally Stable Extremal Region Current Response Signal Voltage Excitation Signal Machine Fault Diagnosis

Author Community:

  • [ 1 ] [Jiao, Zhaonan]Beijing Univ Technol, Beijing 100124, Peoples R China
  • [ 2 ] [Zhang, Xiliang]Beijing Univ Technol, Beijing 100124, Peoples R China
  • [ 3 ] [Sun, Yuqi]Beijing Univ Technol, Beijing 100124, Peoples R China
  • [ 4 ] [Hua, Xia]Beijing Univ Technol, Beijing 100124, Peoples R China
  • [ 5 ] [Li, Huayu]Beijing Univ Technol, Beijing 100124, Peoples R China

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Source :

2022 SIGNAL PROCESSING: ALGORITHMS, ARCHITECTURES, ARRANGEMENTS, AND APPLICATIONS (SPA)

ISSN: 2326-0262

Year: 2022

Page: 76-80

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 9

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