Quality-relevant dynamic process monitoring based on dynamic total slow feature regression model

Abstract

Slow feature analysis (SFA) can extract the features representing intrinsic properties. Moreover, in an actual industrial process: information can be found in low-frequency data signals. Hence, SFA detects subtle changes more sensitively than other algorithms do. On this basis, a dynamic total slow feature regression is proposed to achieve efficient quality-relevant fault detection for dynamic processes. Initially, lagged measurements are introduced to process variables as additional variables for further obtaining the dynamic information of the industrial process. Then, to solve the information redundancy problem and improve the quality-relevant fault detection performance, total projection method is utilized to divide the process variables into two parts, which are relevant to and independent from quality. Finally, monitoring statistics on two subspaces are constructed to detect quality-relevant and irrelevant faults. The experiment results on the Tennessee Eastman process demonstrates that the proposed method outperforms some other state-of-the-art methods.