Computer-aided detection of airgap eccentricity in operating three-phase induction motors by Park's vector approach

1993 ◽  
Vol 29 (5) ◽  
pp. 897-901 ◽  
Author(s):  
A.J.M. Cardoso ◽  
E.S. Saraiva
Keyword(s):  
Induction Motors ◽  
Computer Aided Detection ◽  
Three Phase ◽  
Computer Aided ◽  
Vector Approach

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

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Lipeng Wei ◽  
Xiang Rong ◽  
Haibo Wang ◽  
Shuohang Yu ◽  
Yang Zhang
Keyword(s):  
Machine Vision ◽  
Induction Motors ◽  
Local Binary Patterns ◽  
Image Features ◽  
Identification Accuracy ◽  
Detection Methods ◽  
Automatic Identification ◽  
Rotor Faults ◽  
Three Phase ◽  
Vector Approach

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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