DocumentCode :
251689
Title :
Condition monitoring of induction motor using Artificial Neural Network
Author :
Bhavsar, Ravi C. ; Patel, Rakesh A. ; Bhalja, B.R.
Author_Institution :
Dept. of Electr. Eng., Ganpat Univ., Kherva, India
fYear :
2014
fDate :
24-26 July 2014
Firstpage :
1
Lastpage :
6
Abstract :
This paper deals with stator fault detection of induction motor. Mathematical modeling of induction motor for healthy and stator fault condition are explained. In this paper Artificial Neural Network technique is applied for stator fault detection in induction motor. By collecting the simulation data from the mathematical model developed in MATLAB simulink, ANN is trained. 16 different parameters of induction motor have been taken to train the neural network. ANN gives best performance with 10 neurons in hidden layer. The results clearly show that trained neural network can precisely detect the faults before any major problem occurs.
Keywords :
condition monitoring; fault diagnosis; induction motors; maintenance engineering; neural nets; power engineering computing; reliability; Matlab; Simulink; artificial neural network; condition monitoring; healthy condition; induction motor; stator fault condition; stator fault detection; Artificial neural networks; Equations; Fault detection; Induction motors; Mathematical model; Stator windings; Analytical model; Artificial Intelligence; Artificial Neural Network; Condition Monitoring; Induction motor;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Emerging Research Areas: Magnetics, Machines and Drives (AICERA/iCMMD), 2014 Annual International Conference on
Conference_Location :
Kottayam
Print_ISBN :
978-1-4799-5201-4
Type :
conf
DOI :
10.1109/AICERA.2014.6908207
Filename :
6908207
Link To Document :
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