Towards robust fault detection for industrial processes with a hybrid feature fusion and ensemble learning framework

Early fault identification and evaluation are crucial to ensure the efficiency, safety, and reliability of the industrial process. With the rapid growth of process data in modern industries, machine learning and data-driven methods have become indispensable for effective process monitoring and fault diagnosis. This study proposes a fault detection framework that effectively leverages feature fusion and ensemble learning to boost monitoring performance under intricate industrial conditions. The proposed method combines Slow Feature Analysis (SFA), Kernel SFA (KSFA), and Dynamic SFA (DSFA) to extract distinctive features that accurately reflect linear, nonlinear, and dynamic changes in process data. Furthermore, independent applications of ensemble learning techniques, such as majority and weighted voting, can further increase the reliability of identifying faults with the help of statistical monitoring metrics. The effectiveness of this approach is confirmed using the Tennessee Eastman (TE) benchmark dataset alongside real-world data from a wastewater treatment facility in Beijing. The study spans simulated and real industrial settings to develop a robust framework for fault detection in dynamic and nonlinear processes. The results show that feature fusion and ensemble learning outperform single-model approaches, offering higher sensitivity and reliability. The framework demonstrates strong potential to reduce false alarms, improve anomaly detection, and enhance both efficiency and safety in industrial operations.