文献类型：期刊文献

英文题名：Parallel PCA-KPCA for nonlinear process monitoring

作者：Jiang, Qingchao[1];Yan, Xuefeng[1]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

年份：2018

卷号：80

起止页码：17

外文期刊名：CONTROL ENGINEERING PRACTICE

收录：;EI(收录号：20183405732544);WOS:【SCI-EXPANDED(收录号:WOS:000447483500002)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61603138, in part by the Shanghai Pujiang Program, China under Grant 17PJD009, in part by Fundamental Research Funds for the Central Universities, China under Grants 222201717006 and 222201714027, in part by the Programme of Introducing Talents of Discipline to Universities, China (the 111 Project) under Grant B17017.

语种：英文

外文关键词：Nonlinear process monitoring; Fault detection; Parallel PCA-KPCA; Randomized algorithm; Genetic algorithm

摘要：Both linear and nonlinear relationships may exist among process variables, and monitoring a process with such complex relationships among variables is imperative. However, individual principal component analysis (PCA) or kernel PCA (KPCA) may not be able to characterize these complex relationships well. This paper proposes a parallel PCA-KPCA (P-PCA-KPCA) modeling and monitoring scheme that incorporates randomized algorithm (RA) and genetic algorithm (GA) for efficient fault detection for a process with linearly correlated and nonlinearly related variables First, to determine the included variables in the parallel PCA (P-PCA) and the parallel KPCA (P-KPCA) models, GA-based optimization is performed, in which RA is used to generate faulty validation data. Second, monitoring statistics are established for the P-PCA and the P-KPCA models, in which the process status is determined. The proposed monitoring scheme discriminates the linear and nonlinear relationships among variables in a process and deals with nonlinear processes efficiently. We provide case studies on a numerical example and the continuous stirred tank reactor process. These case studies demonstrate that the proposed P-PCA-KPCA monitoring scheme is better than conventional PCA- or KPCA-based methods at performing nonlinear process monitoring.