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

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.

Figure optionsDownload as PowerPoint slideHighlights
► Wavelet based Neuro-Detector approach is used to detect the aging indications of an electric motor's vibration signals.
► Analysis of the aging indications, which arise from bearing damage, is performed in the low frequency region of spectra.
► Multi Resolution Wavelet Transform and Auto Associative Neural Network methods are combined to detect indications.
► Using the AANN's error variation, some characteristic frequencies are listed and their physical identifications are given.
► A Wavelet based Neuro-Detector is used to track the bearing fluting mechanism.