Recursive cointegration analytics for adaptive monitoring of nonstationary industrial processes with both static and dynamic variations. (August 2020)

• Record Type: Journal Article
• Authors: Yu, Wanke; Zhao, Chunhui; Huang, Biao
• Is Part Of: Journal of process control. Volume 92(2020)
• Page Start: 319
• Page End: 332
• Publication Date: 2020-08
• Subjects: Adaptive process monitoring -- Nonstationary processes -- Cointegration analysis -- Long-term equilibrium relationship; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2020.06.013
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: Conventional adaptive monitoring strategies detect anomalies in time-varying process by frequently updating models, which requires high computation complexity and may falsely include abnormal samples. Cointegration analysis (CA) based monitoring strategies can be implemented with less model updating since they are developed based on the extracted long-term equilibrium relationship. However, once the cointegration relationship changes, the previous CA model cannot accurately reflect the operation status of future nonstationary process. In this study, an adaptive monitoring scheme based on recursive CA is proposed to address the aforementioned issues for nonstationary processes. First, a recursive strategy is developed for CA to effectively update the monitoring model. After that, three monitoring statistics are developed to reflect the operation status of the industrial process with representation of both static deviation and dynamic fluctuation. Finally, an adaptive monitoring strategy is constructed based on the proposed recursive CA using the aforementioned monitoring statistics. Experimental results of two real industrial processes show that the adaptive monitoring strategy based on recursive CA can effectively adapt to normal process changes without frequent model updating. Highlights: In comparison with conventional adaptive methods, the proposed method can detect anomalies in time-varying processes with less model updating. With representation of both static deviation and dynamic fluctuation, the false alarms can be effectively reduced and the monitoring model can be correctly updated to adapt to new cointegration relationship. The performance of the proposed method is illustrated using two real industrial processes, and the experimental results show that it can effectively adapt to normal process changes without frequent model updating.