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Fault Detection and Diagnosis of Induction Machines based on Wavelet and Probabilistic Neural Network

by Pu Shi, Zheng Chen, Yuriy Vagapov
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 69 - Number 14
Year of Publication: 2013
Authors: Pu Shi, Zheng Chen, Yuriy Vagapov
10.5120/11914-8034

Pu Shi, Zheng Chen, Yuriy Vagapov . Fault Detection and Diagnosis of Induction Machines based on Wavelet and Probabilistic Neural Network. International Journal of Computer Applications. 69, 14 ( May 2013), 44-50. DOI=10.5120/11914-8034

@article{ 10.5120/11914-8034,
author = { Pu Shi, Zheng Chen, Yuriy Vagapov },
title = { Fault Detection and Diagnosis of Induction Machines based on Wavelet and Probabilistic Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { May 2013 },
volume = { 69 },
number = { 14 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 44-50 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume69/number14/11914-8034/ },
doi = { 10.5120/11914-8034 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:30:18.481214+05:30
%A Pu Shi
%A Zheng Chen
%A Yuriy Vagapov
%T Fault Detection and Diagnosis of Induction Machines based on Wavelet and Probabilistic Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 69
%N 14
%P 44-50
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, a prototype wavelet and probabilistic based neural network classifier for recognizing rotor bar defects is implemented and tested under various transient signals. The wavelet transform (WT) technique is integrated with the neural network model to extract rotor fault features. Firstly, the multiresolution analysis technique of WT and the particle swarm optimization (PSO) theorem are employed to extract the features of the distorted signal. Then, the probabilistic neural network (PNN) classifies these extracted features to identify the rotor defects type. The proposed approach can reduce a great quantity of the distorted signal features without losing its original property. Moreover, less memory space and computing time are required. Various experimental cases tested results show that the hybrid classifier can detect and classify broken rotor bar faults efficiently.

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Index Terms

Computer Science
Information Sciences

Keywords

Induction machine WT PSO PNN Broken rotor bar

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