Online monitoring of performance variations and process dynamic anomalies with performance-relevant full decomposition of slow feature analysis. (August 2019)

• Record Type: Journal Article
• Authors: Zheng, Jiale; Zhao, Chunhui
• Is Part Of: Journal of process control. Volume 80(2019)
• Page Start: 89
• Page End: 102
• Publication Date: 2019-08
• Subjects: Process monitoring -- Closed-loop control -- Fine-scale subspace decomposition -- Process dynamics -- Performance-relevant subspace -- Process-relevant subspace; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2019.05.004
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: A fine-scale decomposition of process variations is proposed for closed-loop system supervised by process performance. The influences of control actions on process performance and dynamics are concurrently analyzed for process monitoring. Process static and dynamic information are separately analyzed for detection of different operating conditions. The validity of the proposed method is illustrated with two industrial processes. Abstract: Closed-loop control is widely used in modern industrial processes to ensure that process performance such as product quality and waste discharge are maintained at the predefined set-points. However, control actions which may compensate the disturbances on process performance and result in obvious process dynamics cause great challenges to process monitoring. The influences of control actions on process performance and dynamics for process monitoring have not been fully investigated before. Thus, a performance-relevant full decomposition of slow feature analysis termed PFDSFA here, is proposed for process monitoring under closed-loop control by simultaneously considering the influences of process variations on process performance and dynamics. First, the new algorithm extracts variations which are closely relevant to performance variables using canonical correlation analysis. Based on it, process variable space can be decomposed into performance-relevant subspace and process-relevant subspace. Then, both static and dynamic variations of each subspace are extracted to design monitoring statistics which can distinguish normal operating condition deviations from dynamic anomalies incurred by real faults. The proposed PFDSFA algorithm offers a fine-scale decomposition of process variations and achieves comprehensive process monitoring of process static and dynamic characteristics. Besides, it is efficient in indicating whether disturbances make influence on process performance and dynamics. Finally, two case studies are employed to illustrate the applicability and efficacy of the proposed algorithm in comparison with some other monitoring approaches.