A Neural Network Approach to Bearing Health Assessment

Abstract

Vibration measurement has been widely applied to bearing condition monitoring and health assessment. To device a method for signal interpretation and automate the process of defect severity classification under varying operating conditions, a multilayer feed-forward neural network has been developed. A health index based on the Weibull theory has been proposed for defect severity assessment. Feature vectors extracted from the wavelet transform and spectral post-processing of the vibration data were used as inputs to the neural network. The designed neural network has shown to be able to effectively differentiate faulty bearings from a comparatively "healthy" bearing, identify defective elements, and classify the defect severity using a corresponding health index value. A classification rate of 99% and 97% were achieved for defects in the inner and outer raceways, respectively. The results encourage further exploration of various neural network structures for automated bearing health diagnosis under varying operating conditions.