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

Karatoprak E., Sengueler T., Seker S.

IEEE 16th Signal Processing and Communications Applications Conference, Aydın, Türkiye, 20 - 22 Nisan 2008, ss.356-359 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Basıldığı Şehir: Aydın
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.356-359
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

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.