Parallel PCA–KPCA for nonlinear process monitoring

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 w...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Control engineering practice Ročník 80; s. 17 - 25
Hlavní autori: Jiang, Qingchao, Yan, Xuefeng
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.11.2018
Predmet:
Fault detection
Genetic algorithm
Nonlinear process monitoring
Parallel PCA–KPCA
Randomized algorithm
Nonlinear process monitoring
Fault detection
Randomized algorithm
Genetic algorithm
Parallel PCA–KPCA
ISSN:0967-0661, 1873-6939
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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. •A parallel PCA–KPCA-based nonlinear process monitoring scheme is proposed.•RA and GA are integrated to emplace variables automatically.•Linear and nonlinear variable relationships are well characterized.•Monitoring efficiency and superiority are demonstrated.
ISSN:0967-0661
1873-6939
DOI:10.1016/j.conengprac.2018.07.012