Enhanced multicorrelation block process monitoring and abnormity root cause analysis for distributed industrial process: A visual data-driven approach. (October 2022)

• Record Type: Journal Article
• Authors: Zhu, Qun-Xiong; Wang, Xin-Wei; Li, Kun; Xu, Yuan; He, Yan-Lin
• Is Part Of: Journal of process control. Volume 118(2022)
• Page Start: 1
• Page End: 15
• Publication Date: 2022-10
• Subjects: Process monitoring -- Hierarchical clustering -- Multiple correlation blocks -- Maximum information coefficient -- Tennessee-Eastman process; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2022.08.008
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: With the rapid expansion of the scale of modern industrial processes, more and more machine learning approaches using process variables for process monitoring and alarm analysis. The complex correlation of these variables makes a purely process knowledge-based variable division method unsatisfactory for process monitoring. To address this problem, a distributed process monitoring and abnormity root cause analysis model is built from a data-driven perspective. The proposed hierarchical clustering-based multicorrelation block partial least squares (HCMCB-PLS) divides the whole process into several blocks by using hierarchical clustering (HC), and the maximum information coefficient (MIC) is performed to select the correlation variables between the sub-blocks. PLS is conducted in each sub-block for process monitoring. Besides, a modified contribution-based abnormity root cause analysis strategy is developed, which uses an online distributed contribution analysis method to track the root cause variables. The effectiveness of proposed HCMCB-PLS is validated through a case study on the Tennessee-Eastman process. Comparative simulation results indicate that the HCMCB-PLS methodology outperforms other models in both industrial process monitoring and abnormity root cause analysis. Highlights: A distributed process monitoring and root cause analysis model is proposed. Simulations of process monitoring and root cause analysis are carried out. Simulation results verify the effectiveness of the proposed methodology.