Research on Few-Shot Bearing Fault Diagnosis Method Based on Time-Frequency Feature Fusion and Relation Networks
Aiming at the low fault diagnosis accuracy caused by insufficient fault samples of rolling bearings and incomplete feature information acquisition,this paper proposes a few-shot fault diagnosis method based on time-frequency feature fusion and relation networks.This method combines a meta-learning training strategy.Firstly,a feature extraction module is designed to obtain and fuse the time-frequency domain information of rolling bearing vibration signals,which enhances the comprehensiveness of the extracted features.Secondly,a metric module of the relation network is used to calculate the similarity scores between support samples and query samples,ultimately achieving fault diagnosis.Experimental results demonstrate that in cross-working condition scenarios of the CWRU dataset,this method exhibits outstanding performance,with a maximum fault diagnosis accuracy of 99.82%.Additionally,it effectively verifies the validity of the feature extraction module,significantly improving the accuracy and reliability of rolling bearing fault diagnosis.
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