SVM-tree and SVM-forest algorithms for imbalanced fault classification in industrial processes

Abstract

Fault classification plays a central role in process monitoring and fault diagnosis in complex industrial processes. Plenty of fault classification methods have been proposed under the assumption that the sizes of different fault classes are similar. However, in practical industrial processes, it is a common case that large amounts of normal data (majority) and only few fault data (minority) are collected. In other words, most existing fault classification problems were carried out under the imbalanced data scenario, which will lead to a restricted performance of traditional classification algorithms. In this paper, a K-means based SVM-tree algorithm is proposed to deal with the nonlinear multiple-classification problem under the situation of imbalance data. Meanwhile, a SVM-forest scheme is further developed for sensitive data selection and performance enhancement when the imbalance degree is larger among different classes. Effectiveness of the proposed method is verified through the Tennessee Eastman (TE) benchmark process.