Hybrid vibration signal monitoring approach for rolling element bearings

Abstract

New approach to identify different lifetime stages of rolling element bearings, to improve early bearing fault detection, is presented. We extract characteristic features from vibration signals generated by rolling element bearings. This data is first pre-labelled with an unsupervised clustering method. Then, supervised methods are used to improve the labelling. Moreover, we assess feature importance with each classifier. From the practical point of view, the classifiers are compared on how early emergence of a bearing fault is being suggested. The results show that all of the classifiers are usable for bearing fault detection and the importance of the features was consistent.