A Study on Continuous Wavelet Transform for Fault Detection in Electric Motors

The purpose of this paper is to extract features from vibration signals measured from induction motors subjected to accelerated bearing fluting aging. The signals taken from accelerometers placed near to process end bearing were first combined using simple sensor fusion method and then spectral analysis and time-scale analysis were performed. Fused vibration signals were decomposed into several scales using continuous wavelet transform analysis and selected scales was further investigated to get detailed information relating to bearing damage features. And also the advantage of the continuous wavelet transform over Fourier transform was emphasized in terms of getting the bearing damage between 2-4 kHz and this frequency band was interpreted as a joint feature for both of the healthy and aged motor cases. And also, the transfer function to indicate the bearing damage was represented