An Active–Reactive Power Method for the Diagnosis of Rotor Faults in Three-Phase Induction Motors Operating Under Time-Varying Load Conditions

2012 ◽  
Vol 27 (1) ◽  
pp. 71-84 ◽  
Author(s):  
Sérgio M. A. Cruz
Keyword(s):  
Reactive Power ◽  
Induction Motors ◽  
Time Varying ◽  
Power Method ◽  
Rotor Faults ◽  
Three Phase ◽  
Load Conditions

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.

scholarly journals Classification and Detection of Faults in Induction Motor using Dwt with Deep Learning Methods under the Time-Varying and Constant Load Conditions

2019 ◽  
Vol 8 (3) ◽  
pp. 1413-1418
Keyword(s):  
Neural Network ◽  
Induction Motor ◽  
Reactive Power ◽  
Induction Machine ◽  
Load Condition ◽  
Constant Load ◽  
Discrete Wavelet ◽  
Time Varying ◽  
Squirrel Cage ◽  
Three Phase

This article proposed a method to detect the faults in multi-winding induction motor using Discrete Wavelet transform combined with Deep Belief Neural Network (DBNN). This technique relies on the instantaneous reactive power signal decomposition, from which detail coefficients and wavelet approximations are extracted which are termed as features. In order to obtain a robust diagnosis, this article proposed to classify the feature vectors extracted from DWT analysis of power signals using DBNN (Deep Belief Neural Network) to distinguish the motors state. Subsequently, in order to validate the proposed approach, a three phase squirrel cage induction machine is simulated under MATLAB software. To check the effectiveness of the proposed method of fault diagnosis the motor is simulated in different simulation environments like time varying load and constant load condition. Promising results were obtained and the performance of DBNN i.e. 99.75% accuracy. The proposed algorithm is compared with various other state-of-art methods and the comparison proves its robustness in diagnosing the fault in motors.

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