Kernel Fisher discriminant analysis for bearing fault diagnosis

Abstract

A kernel Fisher discriminant (KFD) method is applied to the bearing fault diagnosis (i.e. classification of multiple fault classes). This paper deals with KFD for two multi-class fault recognition examples. One example is to recognize faults on different bearing elements; another is to recognize four different severities of the ball faults. The time-domain vibration signals of normal bearings, bearings with different faults have been used for feature extraction. The features are obtained from direct processing of the signal segments using simple preprocessing. The classification results demonstrate that KFD method is effective on the examples. Furthermore, in terms of classification performance, KFD method competes with support vector machines.