This paper proposes a novel technique combining datadriven and model-based techniques to significantly improve the performance in bearing fault diagnostics. Features that provide best classification performance for the given data are selected from a combined set of data driven and model based features. Some of the common data driven techniques from time, frequency and time-frequency domain are considered. For model based feature extraction, recently developed cross-sample entropy is used. The ranking and performance of each of these feature sets are studied, when used independently and when used together. Mutual information based technique is used for ranking and selection of the optimal feature set. Using this method, the contribution to performance and redundancy of each of the data driven features and model based features can be studied. This method can be used to design an effective diagnostic system for bearing fault detection.

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