Enhanced Random Forest With Concurrent Analysis of Static and Dynamic Nodes for Industrial Fault Classification

In recent years, machine learning algorithms have been successfully applied to industrial processes. However, the concurrent analysis of static and dynamic representations has not been comprehensively addressed for industrial process fault classification. In this paper, an enhanced random forest algorithm with a concurrent analysis of static and dynamic nodes is proposed to address this issue for fault classification. First, the standard slow feature analysis is modified by designing a new slowness index that is more suitable for a supervised fault classification problem. Second, a feature ranking process is conducted to determine the significant features. These features, which substitute the raw variables in the nodes, are used to build the enhanced random forest. Using this scheme, the significant static and dynamic nodes are selected to enhance the discriminative ability and interpretation. Additionally, the slow features that are uncorrelated are more suitable for training the forest than the initial correlated variables, and the dynamic characteristics of industrial processes are thus comprehensively addressed. The application of the proposed method to fault classification is evaluated by both the Tennessee Eastman benchmark and a real-world three-phase flow process. The experimental results show that the proposed method outperforms the traditional learning algorithms with remarkable accuracy and F1 score that both exceed 70% for the 16-class Tennessee Eastman process and exceed 99% for the 4-class three-phase flow process. The selected significant features reveal that both the static and dynamic information play important roles in fault classification.