Enhanced dictionary learning based sparse classification approach with applications to planetary bearing fault diagnosis

To date, planetary bearings remain challenging for machinery fault diagnosis because of their intricate kinematics, time-variant modulations, and strong interferences. To address this challenge, this study presents an enhanced dictionary learning based sparse classification (EDL-SC) approach to diagnose planetary bearings. Our main novelty lies in that the data augmentation and dictionary learning strategies are incorporated into our proposed EDL-SC approach, which can significantly enhance the representation ability and recognition ability for the sparse classification-based intelligent diagnosis criterion. Firstly, vibration data augmentation is implemented with an overlapping segmentation strategy to enhance the quality of training samples. Secondly, data-driven dictionary design is achieved by means of dictionary learning, which learns sub-dictionaries and adaptively designs the whole dictionary through considering both the inter-class and intra-class features. Thirdly, a sparse classification strategy is established for intelligent diagnostics by the aid of a discrimination criterion of minimal reconstruction errors. The feasibility and advantage of EDL-SC have been thoroughly evaluated with a challenging planetary bearing dataset. Experiment verification results of planetary bearing fault diagnosis indicate that EDL-SC obtains a superior diagnosis accuracy of 99.63%, strong robustness to noises, and competitive computation efficiency over advanced deep learning and sparse representation classification methods. This work can bring new insights for the application of sparse representation theory from the perspective of pattern recognition, and shows great potentials of EDL-SC for data-driven machinery fault diagnosis.