Using Probabilistic Neural Networks with Wavelet Transform and Principal Components Analysis for Motor Fault Detection

被引：0

作者：

Karatoprak, Erinc ^{[1
]}

Sengueler, Tayfun ^{[1
]}

Seker, Serhat ^{[1
]}

机构：

[1] Istanbul Tech Univ, Elekt Muhendisligi Bolumu, Elekt Elekt Fak, Istanbul, Turkey

来源：

2008 IEEE 16TH SIGNAL PROCESSING, COMMUNICATION AND APPLICATIONS CONFERENCE, VOLS 1 AND 2 | 2008年

关键词：

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This study represents an application of probabilistic neural networks along with multi resolution wavelet analysis, and principal components analysis to an induction motor which was applied to an accelerated aging process according to IEEE standard test procedures. In this manner, the algorithm first applies a multiresolution wavelet analysis to the vibration signals with Shannon entropy to calculate the feature vectors Then, principal components analysis is applied to the feature vectors, reducing the dimensionality for the condition monitoring classification that is to be made by the probabilistic neural networks. The application results show extremely high success rate, thus the study is vital in the scope of reliability.

引用

页码：356 / 359

页数：4

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