Batch process monitoring based on multilevel ICA-PCA  

作　　者：Zhi-qiang GE Zhi-huan SONG 

机构地区：[1]State Key Lab of Industrial Control Technology, Institute of Industrial Process Control, Zhejiang University, Hangzhou 310027, China

出　　处：《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》2008年第8期1061-1069,共9页浙江大学学报（英文版）A辑（应用物理与工程）

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

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.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.I^2 T^2 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.

关 键 词：MULTILEVEL Independent component analysis （ICA） Principal component analysis （PCA） Batch process monitoring NON-GAUSSIAN 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]