Dynamic classification method of fault indicators for bearings’ monitoring

¹ CReSTIC, University of Reims Champagne Ardenne, Moulin de la Housse, 51687 Reims Cedex 2, France
² GRESPI, University of Reims Champagne Ardenne, Moulin de la Housse, 51687 Reims Cedex 2, France

^a Corresponding author: sanaa.kerroumi@univ-reims.fr

Received: 14 December 2012
Accepted: 10 April 2013

Abstract

This paper introduces a dynamic classification method inspired by DBSCAN clustering method for machine condition monitoring in general and for bearings in particular. This method has been developed for two purposes; first to monitor the health condition of a bearing in real time and second to study the behavior of defected rolling element bearing. To fulfill those purposes, the temporal indicator RMS (Root Mean Square) has been chosen as an indicator of the bearing health condition; this indicator has been computed from signals extracted from an experimental bench by two piezoelectric sensors placed radially and axially. The decision upon the right classification method was taken after a comparative study between two classical of the clustering methods (K-means and Density Based Spatial Clustering of Applications with Noise DBSCAN), which led to the conclusion that DBSCAN is more adapted to vibratory signals. DBSCAN was re-adapted to follow any changing in bearings behavior.