Combining kernel principal component analysis and spatial group-wise enhance convolutional neural network for fault recognition of rolling element bearings

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Data-driven deep learning methods have been widely used in the fault diagnosis of rolling bearings, while general network structures are complex with numerous parameters and computationally intensive calculations, leading to limited real-time performance and delayed fault detection. To address these challenges, this paper presents a novel hybrid framework, termed FKP-SGECNN, for efficient and accurate bearing fault identification. The proposed framework combines the strengths of kernel principal component analysis (KPCA), Fisher criterion, spatial group-wise enhance network (SGENet), and convolutional neural network. In the proposed framework, FKP incorporates Fisher criterion to optimize the kernel functions in KPCA, effectively reducing information redundancy in the input data. Furthermore, SGENet is integrated to streamline the network structure and enhance the model’s generalization capability, while maintaining high diagnostic accuracy. The performance of the hybrid framework implies a great potential, which was evaluated by several case studies using multi-class data of bearing faults.