Application of an Oversampling Method Based on GMM and Boundary Optimization in Imbalance-Bearing Fault Diagnosis

Synthetic minority oversampling (SMOTE) has been widely used in dealing with the imbalance classification in the mechanical fault diagnosis field. However, the classical SMOTE model generates poor quality data, which leads to a low diagnostic accuracy of the classification model. This article proposes an oversampling generation model based on the Gaussian mixture model (GMM) and boundary joint optimization (BDOP-GMM-SMOTE). First, GMM is utilized to cluster minority class bearing fault data, and weights of different classes should be distributed according to the cluster density distribution function. Then, the minority classes should be resampled to balance intraclass. Third, the data boundary is established by calculating intraclass and interclass distances between the minority class and other failure-class samples. And penalty coefficient is introduced to optimize data generation boundary by minimizing intraclass and maximizing interclass principle. Afterward, a generated fault dataset satisfied intraclass and interclass balance is obtained. Finally, the generation effectiveness and robustness of the proposed method are verified by bearing experiment data, especially in diagnostic accuracy and processing speed.