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Method for Identifying Stator and Rotor Faults of Induction Motors Based on Machine Vision

Author

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  • Lipeng Wei
  • Xiang Rong
  • Haibo Wang
  • Shuohang Yu
  • Yang Zhang

Abstract

The detection results need to be analyzed and distinguished by professional technicians in the fault detection methods for induction motors based on signal processing and it is difficult to realize the automatic identification of stator and rotor faults. A method for identifying stator and rotor faults of induction motors based on machine vision is proposed to solve this problem. Firstly, Park’s vector approach (PVA) is used to analyze the three-phase currents of the motor to obtain Park’s vector ring (PVR). Then, the local binary patterns (LBP) and gray level cooccurrence matrix (GLCM) are combined to extract the image features of PVR. Finally, the vectors of image features are used as input and the types of induction motor faults are identified with the help of a random forest (RF) classifier. The proposed method has achieved high identification accuracy in both the Maxwell simulation experiment and the actual motor experiment, which are 100% and 95.83%, respectively.

Suggested Citation

  • Lipeng Wei & Xiang Rong & Haibo Wang & Shuohang Yu & Yang Zhang, 2021. "Method for Identifying Stator and Rotor Faults of Induction Motors Based on Machine Vision," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-13, January.
    • Handle: RePEc:hin:jnlmpe:6658648
    DOI: 10.1155/2021/6658648
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    Cited by:

    1. Oleg Gubarevych & Juraj Gerlici & Oleksandr Kravchenko & Inna Melkonova & Olha Melnyk, 2023. "Use of Park’s Vector Method for Monitoring the Rotor Condition of an Induction Motor as a Part of the Built-In Diagnostic System of Electric Drives of Transport," Energies, MDPI, vol. 16(13), pages 1-14, July.

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