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

Lipeng Wei | Zendy; Rong Xiang | Zendy; H. Wang | Zendy; YU Shuohang | Zendy; Yang Zhang | Zendy

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

Author(s) -

Lipeng Wei,

Rong Xiang,

H. Wang,

YU Shuohang,

Yang Zhang

Publication year - 2021

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2021/6658648

Subject(s) - stator , induction motor , rotor (electric) , engineering , support vector machine , artificial intelligence , identification (biology) , control engineering , control theory (sociology) , computer science , pattern recognition (psychology) , mechanical engineering , botany , control (management) , voltage , electrical engineering , biology

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.

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