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 samples are given higher attention levels to enhance the classification boundary. Second, the sample generation area is adaptively adjusted according to its attention level, and the generation area for boundary samples is set relatively conservatively to avoid the sample overlapping issue. Finally, the multiple classification models are used for classification testing. Several related experiments show the effectiveness and superiority of the proposed method in handling imbalanced classification tasks by compared with tradition methods.