An improved initialization method of D-KSVD algorithm for bearing fault diagnosis

A novel bearing fault diagnosis method combining feature extraction based on wavelet packets quantifiers and pattern recognition method based on improved initialization method of Discriminative K-SVD (D-KSVD) algorithm is proposed. In D-KSVD algorithm, the representational power of dictionary and discriminative ability of classifier are seriously affected by their initialization values. Therefore, the improved initialization method of D-KSVD is presented and employed for bearing fault diagnosis. The improvement is that during the initialization of training stage, subdictionaries corresponding to each category are trained by K-SVD separately and then the initial dictionary is constructed by cascading the subdictionaries, which can completely represent the characteristics of all categories, and as for the initialization of linear classifier, naive Bayesian classifier is utilized. The experimental results show that under the same parameters the improved D-KSVD has better classification ability compared with traditional D-KSVD and some other classification methods.

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