KPCA-CCA-Based Quality-Related Fault Detection and Diagnosis Method for Nonlinear Process Monitoring

作     者：Wang, Guang Yang, Jinghui Qian, Yucheng Han, Jingsong Jiao, Jianfang 

作者机构：North China Elect Power Univ Dept Automat Baoding Campus Baoding 071003 Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS》 (IEEE Trans. Ind. Inf.)

年 卷 期：2023年第19卷第5期

页      面：6492-6501页

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 08[工学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China Beijing Municipal Natural Science Foundation Hebei Natural Science Foundation [F2019502185] 

主　　题：Kernel Correlation Informatics Process monitoring Training Principal component analysis Matrix decomposition Quality-related canonical correlation analysis (CCA) fault diagnosis kernel principal component analysis (KPCA) nonlinear process monitoring 

摘      要：This work concerns the issue of quality-related fault detection and diagnosis (QrFDD) for nonlinear process monitoring. A kernel principal component analysis (KPCA)-based canonical correlation analysis (CCA) model is proposed in this article. First, KPCA is utilized to extract the kernel principal components (KPCs) of original variables data to eliminate nonlinear coupling among the variables. Then, the KPCs and output are used for CCA modeling, which not only avoids the complex decomposition of kernel CCA but also maintains high interpretability. Afterwards, under the premise of Gaussian kernel, a proportional relationship between process variables sample and kernel sample is introduced, on the basis of which, the linear regression model between process and quality variables is established. Based on the coefficient matrix of the regression model, a nonlinear QrFDD method is finally implemented which has both the data processing capability of nonlinear methods and the form of linear methods. Therefore, it significantly outperforms existing kernel-based CCA methods in terms of algorithmic complexity and interpretability, which is demonstrated by the simulation results of the Tennessee Eastman chemical process.