A New Adaptive Geometric SMOTE for Bearing Imbalanced Fault Diagnosis

In the field of fault diagnosis, data imbalance is a common issue, which leads to the degradation of the recognition rate of faults classification models. The Synthetic Minority Oversampling Technique (SMOTE) can mitigate the impact of data imbalance by generating new fault samples. In this research, an adaptive geometric SMOTE is proposed for bearing fault diagnosis under imbalanced data. First, different sample attention levels are assigned according to the sample distribution, and the edge sa