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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 postprocessing 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.

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