Detection of bearing failures using wavelet transformation and machine learning approach

Abstract

The paper analyzes and compares two forms of wavelet transformation: discrete (DWT) and continuous (CWT) combined with machine learning in the analysis of the bearing failure. It presents the automatic system to detect the anomaly in the rolling bearing based on wavelet analysis of vibration waveforms combined with the set of classical and deep classifiers. The wavelet transformation is used in the stage of pre-processing of the signal for generating the input attributes in the final classification system. The considered structures of the classifiers include 6 classical machine learning tools integrated into an ensemble and a combination of a few deep Convolutional Neural Networks (CNN) to develop the most accurate diagnostics of the bearing. The calculations have been done in Python and Matlab. The results of both approaches DWT and CWT are discussed and compared. They show the high effectiveness of the approach based on the cooperation of wavelet transform and machine learning methods.