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Using probabilistic neural networks with wavelet transform and principal components analysis for motor fault detection

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3 Author(s)
Karatoprak, E. ; Elektrik-Elektron. Fak. Elektrik Mehendisligi Bolumu, Istanbul Teknik Univ., Istanbul ; Senguler, T. ; Seker, S.

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.

Published in:

Signal Processing, Communication and Applications Conference, 2008. SIU 2008. IEEE 16th

Date of Conference:

20-22 April 2008

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