Online monitoring method for multiple operating batch processes based on local collection standardization and multi‐model dynamic PCA. (5th August 2016)

• Record Type: Journal Article
• Authors: Wang, Yajun; Sun, Fuming; Jia, Mingxing
• Is Part Of: Canadian journal of chemical engineering. Volume 94:Number 10(2016)
• Page Start: 1965
• Page End: 1976
• Publication Date: 2016-08-05
• Subjects: multi‐operation -- non‐Gaussian feature -- LCS‐MMDPCA -- fault detection -- ladle furnace (LF) steelmaking process; Chemical engineering -- Periodicals; Technology -- Periodicals; 660.05
• DOI: 10.1002/cjce.22569
• Languages: English
• ISSNs: 0008-4034

Abstract

Abstract: To handle multiple operations and non‐Gaussian problems which widely exist in complex industry processes, a new online monitoring method for multi‐operation batch processes is proposed by combing local collection standardization and multi‐model dynamic principal component analysis (LCS‐MMDPCA). Since a series of operations are often manually manipulated by operators, in general, the statistics of each batch data do not follow Gaussian distribution, which results in a failure for the construction of a multivariate statistical model. To target multiple operations and non‐Gaussian problems, we first split the complex batch processes into a sequence of stages. Subsequently, the data in each stage are clustered according to the operations. Then, we exploit the Local Collection Standardization (LCS) method to make the data belonging to the same cluster obey Gaussian distribution. At last, we adopt MMDPCA to model the complex industry processes with multiple operations and non‐Gaussian features. Experimental results on fault detection in ladle furnace steelmaking process showed the advantages of the proposed method in comparison to multiway kernel principal component analysis (MKPCA) and multiway dynamic principal component analysis (MDPCA).