A novel kernel dynamic inner slow feature analysis method for dynamic nonlinear process concurrent monitoring of operating point deviations and process dynamics anomalies. (February 2022)

• Record Type: Journal Article
• Authors: Xu, Yuemei; Jia, Mingxing; Mao, Zhizhong; Li, Hanqi
• Is Part Of: Journal of process control. Volume 110(2022)
• Page Start: 59
• Page End: 75
• Publication Date: 2022-02
• Subjects: Slow feature analysis -- Dynamic nonlinear process -- Process condition monitoring -- Dynamic inner model; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2021.12.005
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: A novel nonlinear dynamic inner slow feature analysis method is proposed for dynamic nonlinear process concurrent monitoring of operating point deviations and process dynamics anomalies. In this method, the nonlinear correlation and serial autocorrelation are considered meanwhile to extract the serial auto-correlated latent slow features with explicit dynamic representation. In order to approve slow features (SFs) with explicit dynamic representation from nonlinear dynamic process data, a new multi-goal optimization question is formulized with constraints of the extract latent variable catch some variation information and mutually orthogonal. After the nonlinear dynamic inner slow feature analysis model is trained from data, a corresponding detection strategy is also developed to perform process condition concurrent monitoring. Finally, the superiority and effectiveness of the proposed monitoring method are demonstrated by a numerical simulation case and an actual cold rolling mill case. Highlights: A novel method is proposed to extract the SFs with the explicit presentation of dynamic for nonlinear dynamic process data. Auto-regressive prediction to shrink the data variability to improve the ability to detect faults. The concurrent monitoring scheme is organized for dynamic nonlinear process monitoring.