Critical Concurrent Feature Selection and Enhanced Heterogeneous Ensemble Learning Approach for Fault Detection in Industrial Processes

Abstract

Machine learning approaches have been successfully applied to fault detection in complex industrial processes, while it is still challenging to design an optimal feature space and a stable model for various faults. To solve this problem, a hybrid approach with critical concurrent feature selection and enhanced heterogeneous ensemble learning is proposed. First, raw process variables from field instruments and slow features obtained by simple linear transformation are ingeniously combined to construct the concurrent feature space so as to contain static and dynamic information. Then, the critical concurrent feature space of each individual learner is automatically obtained by the optimal selection process, and the feature diversity of ensemble is realized simultaneously. Finally, an enhanced heterogeneous ensemble model is constructed by different optimized individual learners, which effectively improves the classification accuracy and stability in fault detection. The performance of the proposed approach is evaluated in a simulated Tennessee Eastman benchmark and a real-word three-phase flow process. The experimental results illustrate that the performance of each optimized individual learner outperforms classic random forest algorithm. The generalization and stability of the ensemble model are further improved. Compared with traditional classification algorithms, the proposed approach achieves superior performance with accuracy that exceeds 89% for the Tennessee Eastman process and exceeds 88% for the three-phase flow process respectively. Additionally, the selected critical concurrent features indicate that both static and dynamic information play important roles in fault detection of industrial processes.