A Method for Bearing Fault Diagnosis under Variable Working Conditions Based on Multi-teacher Knowledge Distillation
There are distributional differences in the fault data generated by bearings under different operating conditions,leading to poor diagnostic performance of the trained intelligent fault diagnosis model.A method for bearing fault diagnosis under variable working conditions based on multi-teacher knowledge distillation was pro-posed.This method was divided into two phases based on multi-source domain migration.Firstly,multiple work-ing condition knowledge was migrated to the intermediate domain model,then general knowledge of the source do-main was aggregated aggregated and a small number of samples in the target domain were used to fine-tune the model to complete the target domain adaptation,and finally the obtained common features were classified with an integrated classifier and the fault diagnosis was acquired.The experimental results show that the proposed method can achieve fault diagnosis under different working conditions with high accuracy with only a small number of target condition marked samples.
fault diagnosisknowledge distillationmulti-source domain migrationvariable working condi-tions
NETL
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万方数据
