A Novel Weighted Sparse Representation Classification Strategy Based on Dictionary Learning for Rotating Machinery

Rotating machinery is widely applied in industrial fields. However, it generally operates under tough working conditions, which leads to the weak fault features and renders fault diagnosis more difficult. In this case, an emerging method called sparse representation classification (SRC) is proposed to enhance the fault features and identify the fault status. However, the typical SRC theory fails to consider the locality of the test sample and training sample, and the training set generally contains much redundant information, which may reduce the fault recognition accuracy. Moreover, the time-shift deviation of vibration signal cannot be avoided effectively using a typical SRC model. To overcome the above-mentioned problems, a novel SRC model, i.e., weighted SRC based on dictionary learning (DL-WSRC), is proposed. For the training set, different fault signals are learned based on the improved K-singular value decomposition (K-SVD) algorithm, which can not only adaptively update the whole training set but also reduce redundant information so as to enhance the sample fault features. For the test sample, DL-WSRC selects an accurate time-domain parameter using the K-means clustering algorithm and computes the weighted coefficients according to the parameter distance between the test sample and the training samples. Then, it sparsely represents the test sample by solving a weighted $l_{0}$ -norm problem. The goal of weighting is to pay more attention to the locality of the sample so as to improve the recognition accuracy. Finally, according to the results of sparse representation, the fault status can be identified through the correlation analysis, which can effectively solve the time-shift deviation problem. The effectiveness of the proposed method is validated by the experiments of rotating machinery, and the results indicate that the proposed method realizes fault classification with a high accuracy.