Quality-related Process Monitoring of Industrial Processes based on Key Variable-Slow Feature Analysis

Abstract

In the industrial production, for the close-loop control, not all faults will affect product quality. To detect quality related fault effectively, a novel method named key variable-slow feature analysis (KV-SFA) is proposed in this work to extend the SFA algorithm to the domain of online quality-related fault detection. Firstly, key quality related process variables are selected via the combination of the least absolute shrinkage and selection operator (LASSO) method and the mechanism knowledge. Secondly, the SFA is conducted in the key variables space to extract slow features for establishing fault detection model. Then, the monitoring statistics are constructed and the control limits are estimated. Finally, the validity and effectiveness of the proposed KV-SFA method are proved through an industrial process.