Fault Anomaly Detection Method of Rolling Bearing Based on Distillation Learning
Aiming at the problem that it is difficult to diagnose the fault of aero-engine rolling bearing,an intelligent diagnosis method of bearing based on distillation learning is proposed.Firstly,in order to improve the computational efficiency,the compressed vision transformer(ViT)is used as the backbone feature ex-traction network for distillation learning.Secondly,the data of rolling bearing vibration test bench is used to complete the pre-training of teacher network in distillation learning.in the training process of the distillation learning model,in order to make full use of the knowledge of the teacher network,the loss function of the feature knowledge constraint is adopted,and the data preprocessing method of the spectrum transformation is used to improve the diagnostic accuracy of the model.Finally,the proposed model is fully compared and verified on the fault data of rolling bearings of multiple aero-engines.The results show that the proposed method can accurately realize the intelligent diagnosis of rolling bearing faults,and the diagnosis accuracy can reach more than 96%,which fully shows that the proposed method has high practical value.
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