Temporary short circuit detection in induction motor winding using combination of wavelet transform and neural network

Asfani, D. A. and Muhammad, A.K. and Syafaruddin, Syafaruddin and Purnomo, M. H. and Hiyama, Takashi (2012) Temporary short circuit detection in induction motor winding using combination of wavelet transform and neural network. Expert Systems with Applications: An International Journal, 39 . pp. 5367-5375. ISSN 0957-4174

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Official URL: http://dx.doi.org/10.1016/j.eswa.2011.11.048

Abstract

Monitoring system for induction motor is widely developed to detect the incipient fault. Such system is desirable to detect the fault at the running condition to avoid the motor stop running suddenly. In this paper, a new method for detection system is proposed that emphasizes the fault occurrences as temporary short circuit in induction motor winding. The investigation of fault detection is focused on the transient phenomena during starting and ending points of temporary short circuit. The proposed system utilizes the wavelet transform for processing the motor current signal. Energy level of high frequency signal from wavelet transform is used as the input vriable of neural network which works as detection system. Three types of neural networks are developed and evaluated including feed forward neural network (FFNN), Elman neural network (ELMNN) and radial basis functions neural network (RBFNN). The results show that ELMNN is the most simply and accurate system that can recognize all of unseen data test. Laboratory based experimental setup is performed to provide real-time measurement data for this research.

Item Type:Article
Uncontrolled Keywords:temporary short circuit; induction motor winding; wavelet transform; neural network
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
ID Code:6141
Deposited By:Normajihan Abd. Rahman
Deposited On:10 Jan 2017 15:39
Last Modified:10 Jan 2017 15:39

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