Machinery fault diagnosis based on time–frequency images and label consistent K-SVD

Abstract

Wavelet time–frequency analysis has been widely used for machinery fault diagnosis. Mechanical vibration signals can be converted to time–frequency images using wavelet transform, so machinery fault diagnosis can be transformed to the problem of image classification. Label consistent K-SVD algorithm has been proven to be effective in image classification, which incorporates a label consistent term namely discriminative sparse code error into the objective function. Therefore, in this paper, a novel bearing fault diagnosis method based on wavelet time–frequency image and label consistent K-SVD is proposed. Firstly, continuous wavelet transform is utilized to generate wavelet time–frequency images that can fully reflect bearing fault characteristics. Then texture feature extraction based on gray level co-occurrence matrix is implemented on the wavelet time–frequency images. Finally, label consistent K-SVD is conducted for classification of the time–frequency images, and thus bearing fault diagnosis is realized. The experiment results show that the texture features based on gray level co-occurrence matrix of wavelet time–frequency images can effectively extract the fault characteristics of rolling bearings, and label consistent K-SVD performs better than other classification methods based on dictionary learning under the same parameters.