Application of artificial neural networks and principal component analysis on vibration signals for automated fault classification of roller element bearings

Abstract

© 2016, Polish Academy of Sciences Branch Lublin. All rights reserved. The article addresses the implementation of feature based artificial neural networks and vibration analysis for automated roller element bearings faults identification purpose. Vibration features used as inputs for supervised artificial neural networks were chosen based on principal component analysis as one of the possible methods of data dimension reduction. Experimental work has been conducted on a specially designed test rig and on a drive of the Ganz port crane in port of Novi Sad, Serbia. Different scalar vibration features derived from time and frequency domain were used as inputs to fault classifiers. Several types of roller elements bearings faults, at different levels of loads were tested: discrete faults on inner and outer race and looseness. It is demonstrated that proposed set of input features enables reliable roller element bearing fault identification and better performance of applied artificial neural networks.