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Neural networks applied for fault diagnosis of AC motors

K.S., Rama Rao and Yahya , M.A. (2008) Neural networks applied for fault diagnosis of AC motors. In: International Symposium on Information Technology 2008, ITSim, 26 August 2008 through 29 August 2008, Kuala Lumpur.

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Abstract

This paper presents an Artificial Neural Network (ANN) technique to recognize the incipient faults of an AC motor such as a synchronous motor. The proposed ANN-based fault detector is developed using the Resilient Error Back Propagation (RPROP) training algorithm. The fast and reliable method for multilayer neural networks converges much faster than the conventional back propagation algorithm. The main causes to diagnose three major faults are investigated and validated by adopting feed-forward back propagation neural networks. © 2008 IEEE.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:AC motors; Artificial intelligence; Backpropagation; Backpropagation algorithms; Electric fault currents; Information technology; Motors; Multilayer neural networks; Synchronous motors; Vegetation; Artificial neural networks; Back propagation neural networks; Error back propagations; Fault detectors; Fault diagnosis; Incipient faults; Reliable methods; Training algorithms; Neural networks
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Departments / MOR / COE:Departments > Electrical & Electronic Engineering
ID Code:294
Deposited By: Assoc Prof Dr K. S. Rama Rao
Deposited On:03 Mar 2010 01:19
Last Modified:19 Jan 2017 08:26

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