A Novel Park’s Vector Approach for Investigation of Incipient Stator Fault Using MCSA in Three-Phase Induction Motors

2017 ◽  
Author(s):  
Amandeep Sharma ◽  
Shantanu Chatterji ◽  
Lini Mathew
Keyword(s):  
Induction Motors ◽  
Three Phase ◽  
Stator Fault ◽  
Vector Approach

Virtual Instrumentation Based Analysis of Induction Motor

2014 ◽  
Vol 984-985 ◽  
pp. 970-976
Author(s):  
Memala W. Abitha ◽  
V. Rajini
Keyword(s):  
Induction Motor ◽  
Life Time ◽  
Open Circuit ◽  
Virtual Instrumentation ◽  
Repair Work ◽  
Three Phase ◽  
Stationary Reference Frame ◽  
Stator Fault ◽  
Motor Failure ◽  
Vector Approach

The three phase induction motor is a popularly used machine in many of the industries, which is well known for its robustness, reliability, cost effectiveness, efficient and safe operation. The unnoticed manufacturing failure, mistakes during repair work, exceeding life time may be some of the causes of the induction motor failure, which may lead to the unknown shut down time of the industry. The condition monitoring plays important role as it has the influence on the production of materials and profit. In our work, the induction motor is modelled using stationary reference frame and analysed for single phasing stator fault. The techniques used in detecting the single phasing (open circuit) failures are Park’s vector approach and Fast Fourier Transform (FFT). Park’s vector approach is used for detecting the faults occurring at various phases and FFT is used for detecting the faults of the induction motor working under no load and varying loading conditions.

Stator fault analysis of three-phase induction motors using information measures and artificial neural networks

2017 ◽  
Vol 143 ◽  
pp. 347-356 ◽  
Author(s):  
Gustavo Henrique Bazan ◽  
Paulo Rogério Scalassara ◽  
Wagner Endo ◽  
Alessandro Goedtel ◽  
Wagner Fontes Godoy ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Induction Motors ◽  
Fault Analysis ◽  
Information Measures ◽  
Three Phase ◽  
Artificial Neural ◽  
Stator Fault

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.

Application of Probabilistic Neural Networks in Fault Diagnosis of Three-Phase Induction Motors

2013 ◽  
Vol 433-435 ◽  
pp. 705-708 ◽  
Author(s):  
Shuo Ding ◽  
Xiao Heng Chang ◽  
Qing Hui Wu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Induction Motors ◽  
Probabilistic Neural Network ◽  
Short Circuit ◽  
Model And Simulation ◽  
Fast Training ◽  
Three Phase ◽  
Short Circuits ◽  
Stator Fault

In fault diagnosis of three-phase induction motors, traditional methods usually fail because of the complex system of three-phase induction motors. Short circuit is a very common stator fault in all the faults of three-phase induction motors. Probabilistic neural network is a kind of artificial neural network which is widely used due to its fast training and simple structure. In this paper, the diagnosis method based on probabilistic neural network is proposed to deal with stator short circuits. First, the principle and structure of probabilistic neural network is studied in this paper. Second, the method of fault setting and fault feature extraction of three-phase induction motors is proposed on the basis of the fault diagnosis of stator short circuits. Then the establishment of the diagnosis model based on probabilistic neural network is illustrated with details. At last, training and simulation tests are done for the model. And simulation results show that this method is very practical with its high accuracy and fast speed.

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