Differentiation of journal bearing friction states and friction intensities based on feature extraction methods applied on acoustic emission signals

Abstract

Abstract For diagnosis and predictive maintenance of mechatronic systems, monitoring of bearings is essential. An important building block for this is the determination of the bearing friction condition. This paper deals with the possibility of monitoring different journal bearing friction states, such as mixed and fluid friction, and examines a new approach to distinguish between different friction intensities under several speed and load combinations based on feature extraction and feature selection methods applied on acoustic emission (AE) signals. The aim of this work is to identify separation effective features of AE signals to subsequently classify the journal bearing friction states. Furthermore, the acquired features give information about the mixed friction intensity, which is significant for remaining useful lifetime (RUL) prediction. Time domain features as well as features in the frequency domain have been investigated in this work. The combination of several features generates feature spaces. The position of the objects within these spaces has proved that it is possible to differentiate between journal bearing friction states with the use of AE signals and suitable feature extraction methods. In addition, features that indicate different mixed friction intensities have been found.