Decentralized monitoring for large‐scale process using copula‐correlation analysis and Bayesian inference–based multiblock principal component analysis. (1st July 2019)

• Record Type: Journal Article
• Authors: Tian, Ying; Hu, Tian; Peng, Xin; Du, Wenli; Yao, Heng
• Is Part Of: Journal of chemometrics. Volume 33:Number 8(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-07-01
• Subjects: Bayesian inference -- copula‐correlation analysis -- decentralized monitoring -- large‐scale process monitoring -- multiblock principal component analysis (MBPCA); Chemistry -- Mathematics -- Periodicals; Chemistry -- Statistical methods -- Periodicals; 542.85
• DOI: 10.1002/cem.3158
• Languages: English
• ISSNs: 0886-9383

Abstract

Abstract: Due to the massive monitored samples and their features such as strong coupling and time delay in large‐scale industrial process, the decentralized monitoring methods, which divide the variables into several blocks and perform local monitoring in each subblock, have been brought up to handle the complex characteristics. However, most of the existing decentralized methods utilize the correlation degree among variables for block division, while the correlation patterns among variables are ignored. The missing of correlation pattern will influence the monitoring performance. To address this problem, the copula‐correlation analysis, which considers both the correlation degree and the correlation pattern, is adopted for block division; then the principal component analysis (PCA)–based method is used for subblock monitoring, and the Bayesian inference is introduced to achieve decision fusion of fault detection. The superiority and effectiveness of the proposed method are illustrated through comparison studies on a numerical example and the Tennessee Eastman (TE) benchmark process. Abstract : Copula‐correlation analysis, which considers the correlation degree and correlation pattern simultaneously, is adopted for block division. PCA‐based method is used for subblock monitoring. Bayesian inference is introduced to achieve decision fusion of fault detection.