Distributed monitoring for large-scale processes based on multivariate statistical analysis and Bayesian method. (October 2016)

• Record Type: Journal Article
• Authors: Jiang, Qingchao; Huang, Biao
• Is Part Of: Journal of process control. Volume 46(2016:Oct.)
• Page Start: 75
• Page End: 83
• Publication Date: 2016-10
• Subjects: Distributed monitoring -- Multivariate statistical analysis -- Large-scale process -- Bayesian method; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2016.08.006
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: The necessity of distributed monitoring is analyzed theoretically in the statistical framework. A data-based distributed monitoring framework is established for large-scale processes. A performance-driven distributed monitoring scheme is developed. The efficiency and feasibility are demonstrated. Abstract: Large-scale plant-wide processes have become more common and monitoring of such processes is imperative. This work focuses on establishing a distributed monitoring scheme incorporating multivariate statistical analysis and Bayesian method for large-scale plant-wide processes. First, the necessity of distributed monitoring is demonstrated by theoretical analysis on the impact of process decomposition on multivariate statistical process monitoring performance. Second, a stochastic optimization algorithm-based performance-driven process decomposition method is proposed which aims to achieve the best possible monitoring performance from process decomposition aspect. Based on the obtained sub-blocks, local monitors are established to characterize local process behaviors, and then a Bayesian fault diagnosis system is established to identify the underlying process status of the entire process. The proposed distributed monitoring scheme is applied on a numerical example and the Tennessee Eastman benchmark process. Comparison results to some state-of-the-art methods indicate the efficiency and feasibility.