A Method of Feature Extraction from Time-Frequency Images of Vibration Signals in Faulty Bearings for Classification Purposes
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Time-frequency image processing is considered in the context of change detection and diagnosis purposes based on signal processing paradigm. A method for selection and extraction of features from time-frequency is considered and evaluated. New images are obtained by applying a criterion based on the contours generated by the main components of the analyzed time-frequency image. The transformed images are less complex, and could be white and black only. Features based on statistical moments are considered, selected and used to define discriminant functions, in order to improve the results of the classification. The features include the number of the contours, the average area defined by the contours, the variance of the areas and the Renyi entropies. As case study, signals coming from vibration generated by faults in bearings are considered.
vibration, image, time-frequency transform, signal processing, feature selection, classification