Optimal MLP neural network classifier for fault detection of three phase induction motor

Authors:
Vilas N. Ghate;Sanjay V. Dudul
Affiliations:
Faculty in Electrical Engineering, Government College of Engineering, Amravati (MS), India;Applied Electronics Department, Sant Gadge Baba Amravati University, Amravati (MS), India
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

Citing 6
Cited 3

Neural networks application for induction motor faults diagnosis

Mathematics and Computers in Simulation - Special issue: Modelling and simulation of electrical machines, converters and systems
Decision-level fusion based on wavelet decomposition for induction motor fault diagnosis using transient current signal

Expert Systems with Applications: An International Journal
A new approach to intelligent fault diagnosis of rotating machinery

Expert Systems with Applications: An International Journal
Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference

Expert Systems with Applications: An International Journal
An expert system for the diagnosis of faults in rotating machinery using adaptive order-tracking algorithm

Expert Systems with Applications: An International Journal
Induction machine fault detection using clone selection programming

Expert Systems with Applications: An International Journal

Modeling of oxygen mass transfer in the presence of oxygen-vectors using neural networks developed by differential evolution algorithm

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Induction motors bearing fault detection using pattern recognition techniques

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Modified wavelet neural network in function approximation and its application in prediction of time-series pollution data

Applied Soft Computing

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Abstract

Induction motors are critical components in commercially available equipments and industrial processes due to cost effective and robust performance. Under various operating stresses, motors deteriorate their conditions which result into various faults. Early detection and diagnosis of these faults are desirable for online condition assessment, product quality assurance and improved operational efficiency. From the related work reported so far it is observed that researchers used vibration analysis, harmonics present in stator current, chemical analysis, electromagnetic analysis, etc. As these approaches are complex in view of the requirement of precise measurement and mathematical modeling. As compared to analytical methods, AI based schemes are more efficient and accurate. In this paper optimal MLP NN based classifier is proposed for fault detection which is inexpensive, reliable, and noninvasive by employing more readily available information such as stator current. Detailed design procedure for MLP and SOM NN models is given for which simple statistical parameters are used as input feature space and Principal Component Analysis is used for reduction of input dimensionality. Robustness of classifier to noise is verified on unseen data by introducing controlled Gaussian and Uniform noise in input and output.