Batch process monitoring based on multilevel ICA-PCA

作     者：Zhi-qiang GE Zhi-huan SONG 

作者机构：State Key Lab of Industrial Control Technology Institute of Industrial Process Control Zhejiang University Hangzhou 310027 China 

出 版 物：《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 (浙江大学学报（英文版）A辑（应用物理与工程）)

年 卷 期：2008年第9卷第8期

页      面：1061-1069页

核心收录：

学科分类：08[工学] 0835[工学-软件工程] 0802[工学-机械工程] 080201[工学-机械制造及其自动化] 

基　　金：Project (No. 60774067) supported by the National Natural ScienceFoundation of China 

主　　题：Multilevel Independent component analysis (ICA) Principal component analysis (PCA) Batch process monitoring Non-Gaussian 

摘      要：In this paper, we describe a new batch process monitoring method based on multilevel independent component analysis and principal component analysis (MLICA-PCA). Unlike the conventional multi-way principal component analysis (MPCA) method, MLICA-PCA provides a separated interpretation for multilevel batch process data. Batch process data are partitioned into two levels: the within-batch level and the between-batch level. In each level, the Gaussian and non-Gaussian components of process information can be separately extracted. I2, T2 and SPE statistics are individually built and monitored. The new method facilitates fault diagnosis. Since the two variation levels are decomposed, the variables responsible for faults in each level can be identified and interpreted more easily. A case study of the Dupont benchmark process showed that the proposed method was more efficient and interpretable in fault detection and diagnosis, compared to the alternative batch process monitoring method.