Wavelet based Neuro-Detector for low frequencies of vibration signals in electric motors

Duygu Bayram*, Serhat Şeker

*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: ???type-name???Makalebilirkişi

25 Atıf (Scopus)

Özet

This study presents a Wavelet based Neuro-Detector approach employed to detect the aging indications of an electric motor. Analysis of the aging indications, which can be seen in the low frequency region, is performed using vibration signals. More specifically, two vibration signals are observed for healthy and faulty (aged) cases which are measured from the same electric motor. Multi Resolution Wavelet Analysis (MRWA) is applied in order to obtain low and high frequency bands of the vibration signals. Thus for detecting the aging properties in the spectra, the Power Spectral Density (PSD) of the subband for the healthy case is used to train an Auto Associative Neural Network (AANN). The PSD amplitudes, which are computed for the faulty case, are applied to input nodes of the trained network for the re-calling process of AANN. Consequently, the simulation results show that some spectral properties defined in low frequency region are determined through the error response of AANN. Hence, some specific frequencies of the bearing damage related to the aging process are detected and identified.

Orijinal dilİngilizce
Sayfa (başlangıç-bitiş)2683-2691
Sayfa sayısı9
DergiApplied Soft Computing
Hacim13
Basın numarası5
DOI'lar
Yayın durumuYayınlandı - 2013

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