Recursive Exponential Slow Feature Analysis for Fine-Scale Adaptive Processes Monitoring With Comprehensive Operation Status Identification

Due to the compensation of the control loops, industrial processes under feedback control generally reveal typical dynamic behaviors for different operation statuses. Conventional adaptive methods may update model falsely and thus result in invalid monitoring results, since they cannot effectively extract the feedback dynamic information and fail to accurately differentiate real anomalies from normal process changes. In this study, a recursive exponential slow feature analysis (ESFA) algorithm is developed for fine-scale adaptive monitoring to solve the problem of false model updating. First, an ESFA method is proposed to nonlinearly extract slow features, so that the general trend of the process variations can be better captured. On the basis of the ESFA model, a fine-scale adaptive monitoring scheme is developed to accurately capture the normal changes of industrial processes, including normal slow varying and normal shift of operation conditions. In this way, the normal slow varying can be effectively distinguished from incipient faults with unusual dynamic behaviors to avoid falsely adapting for the fault case, and the monitoring model can be correctly updated for new operation status after distinguishing real process anomalies from normal shifts of operation conditions. A simulation process and two real industrial processes are adopted to validate the performance of the proposed adaptive monitoring method. Experimental results show that the proposed method can effectively identify different operation statuses to decide whether to update the monitoring model or to raise an alarm.