Abstract:Traditional fault classification methods mostly assume similar or equal sample sizes for different types of data. However, the bulk of data collected in the actual industrial process is normal with a minority belonging to fault data, which causes data imbalance. Aiming at the imbalanced data, this study proposes the fault classification method combining K-means Bayes with AdaBoost-SVM. Two independent classifiers are designed with the D-S evidence theory to merge the classification results, so as to make up for their weak classification capabilities for certain categories. Experiments show that the fault classification method proposed in this study has higher classification accuracy than single Bayes or SVM.