A multi-fault diagnosis method based on improved SMOTE for class-imbalanced data

With the development of industrial processes, how to effectively diagnose the faults in an increasingly complex production process has attracted widespread attention. It is worth noting that there may be multiple types of faults in the actual industrial process, and there is an extreme class imbalance between the normal samples and the fault samples. Therefore, it is of practical significance to carry out research on the multi-fault diagnosis method for class-imbalanced data. In this paper, a multi-fault diagnosis method based on improved synthetic minority sampling technology (SMOTE) is proposed. First, aiming at the class imbalance, an improved SMOTE algorithm based on Mahalanobis distance (Mahalanobis distance-based SMOTE [MSMOTE]) is proposed for oversampling. As the Euclidean distance in the traditional SMOTE algorithm does not consider the coupling relationship between features, the Mahalanobis distance is introduced, which is not dependent on the scale and eliminates the influence of different dimensions. Second, in order to better obtain the global and local information of the sample, the kernel local Fisher discriminant analysis (KLFDA) algorithm is used for feature extraction. Third, a multi-fault diagnosis model based on the AdaBoost.M2 classifier is constructed in which the decision tree is introduced as the weak classifier. The Adaboost.M2 algorithm integrates multiple decision trees by setting the sample weight, the label weight, and the classifier weight, which effectively improve the classification accuracy by only using the decision tree. Finally, the Tennessee Eastman process is used to conduct case studies. For the comparison results, the proposed multi-fault diagnosis method based on improved SMOTE has higher accuracy and F1-Score.