关键词: 轴承, 知识蒸馏, 迁移学习, 故障诊断

Abstract: In view of the complex and changeable conditions of industrial sites, it is prone to cause data missing or imbalance, resulting in low accuracy of the model. In this paper, a bearing fault diagnosis method with improved knowledge distillation and transfer learning is proposed. Firstly, the teacher-student model adopts dense convolutional neural network and artificial neural network to adaptively and randomly extract key feature information of source domain samples and target domain samples. Secondly, the soft label loss and hard label loss of the corresponding domain are obtained, and hierarchical transfer learning is introduced to improve the difference of conditional distribution of domain samples to obtain the final distillation loss function, and the distilled “dark knowledge” is feedback to update the student model. Finally, the semi-supervised fault transference decision of the intelligences is implemented using the target domain test samples. The experimental results show that the student model is able to learn various properties from the teacher model and improve the diagnostic accuracy of the simple model, and that the method has superior accuracy and robustness compared to other methods.

Key words: bearing, knowledge distillation, transfer leaning, fault diagnosis