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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 …