Dynamic ensemble selection based improved random forests for fault classification in industrial processes

Abstract

Fault classification is an important part in industrial process for process monitoring and control. As an ensemble learning approach for classification, random forests has been widely used in different areas. Taking into account the performance of individual decision tree, the diversity between trees and the difference between process data, a k nearest neighbors-hierarchical clustering (KNN-HC) method is proposed in this paper for dynamic ensemble selection (DES) in random forests. In addition, a weighted probability fusion strategy is developed as an alternative of majority voting rule. The experimental evaluation of the proposed method is carried out through the Tennessee Eastman (TE) benchmark process. Results show that the proposed method outperforms three conventional methods, the original random forests (RF) and the static selection based random forests.