Multidomain Kernel Dictionary Learning Sparse Classification Method for Intelligent Machinery Fault Diagnosis

Sparse representation classification (SRC) has gradually received attention due to its powerful feature representation ability. However, the discriminative ability of traditional SRC methods is highly susceptible to the time-shift characteristic of vibration signals. To overcome the challenge, a multidomain kernel dictionary learning-based sparse classification (MDKDL-SC) method is proposed. First, a novel kernel discriminative dictionary (KDDL) is developed, in which a Gaussian edit distance with a real penalty kernel (ERP) is designed to tackle the time-shift property of the data. Second, a dictionary-based adjustable weighted voting strategy is developed in the recognition stage to leverage the representations learned from multiple domains. The weights of each domain are determined by a cross-validation method, which promotes the recognition performance by voting the weighted prediction label vectors. The performance of MDKDL-SC is validated by two datasets. Experimental results demonstrate that the MDKDL-SC method achieves the recognition rates of 99.75% and 99.52% in the two cases, respectively. Furthermore, the proposed method is compared with some cutting-edge methods, which further confirms the superiority of the MDKDL-SC method in machinery fault diagnosis.