A novel adaptive generalized domain data fusion-driven kernel sparse representation classification method for intelligent bearing fault diagnosis

Dictionary learning has gradually attracted attention due to its powerful feature representation ability. However, the time-shift property of collected signals hinders the recognition of various bearing states. In addition, existing dictionary learning methods are mostly designed based on a single domain, while common data fusion methods used in data-driven cannot be directly extended to dictionary learning. In this paper, a novel adaptive generalized domain data fusion-driven kernel sparse representation classification method (AGDFDK-SRC) is proposed. First, to avoid the effect of the time-shift property on dictionary learning, a class-specific kernel sub-dictionary learning method is proposed, by which the non-linear signal data is mapped into high-dimensional feature space via a kernel trick. Second, the class-specific kernel sub-dictionaries are learned by kernel K-singular value decomposition in a data-driven manner. Then, an adaptive generalized domain data fusion strategy is developed for dictionary learning, which implements data fusion of multiple domain signals to enhance the feature mining ability and representation ability of the learned dictionary. Finally, a kernel sparse classification method is designed to achieve intelligent bearing fault diagnosis. Two bearing datasets are exploited to verify the recognition performance of AGDFDK-SRC, indicating that the AGDFDK-SRC obtains superior average classification accuracies of 98.23% and 99.50%, respectively.