In this paper, a neural network based diagnosis technique is developed to detect fault and estimate the severity of the spalls at the inner race, outer race, roller and cage. To this end, a bearing test rig is developed, in which the normal and faulted bearings with differing spall size at different locations are operated under accelerated loading conditions. Features are extracted from the bearings, which include the time-based indicators such as RMS, peak, crest and kurtosis, frequency based indicators obtained by envelope analysis, and timefrequency based ones like wavelet decomposition. Neural network model is constructed using the features for the classification. The model is then applied to diagnose the fault in the new bearings, which includes the identification of the fault and severity. Particular attention is made to the study of statistical significance to check the validity of the model.
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