A systematic comparison of PCA-based Statistical Process Monitoring methods for high-dimensional, time-dependent Processes

Author:

Rato Tiago1,Reis Marco1,Schmitt Eric2,Hubert Mia2,De Ketelaere Bart3

Affiliation:

1. CIEPQPF, Dept. of Chemical Engineering, University of Coimbra, Pólo II - Rua Sílvio Lima; 3030-790 Coimbra Portugal

2. Dept. of Mathematics, KU Leuven, Celestijnenlaan 200B, BE - 3001; Leuven Belgium

3. MeBioS, Dept. of Biosystems, KU Leuven; Kasteelpark Arenberg 30, BE - 3001 Leuven Belgium

Funder

Portuguese Foundation for Science and Technology

Portuguese FCT

European Unions FEDER

Eixo I do Programa Operacional Factores de Competitividade

QREN

Flanders Agency for Innovation by Science and Technology (IWT)

KU Leuven

IAP Research Network P7/06 of the Belgian Science Policy

Flanders Agency for Innovation by Science and Technology (IWT) through the SBO POM project

Industrial Research Fund of the KU Leuven

Publisher

Wiley

Subject

General Chemical Engineering,Environmental Engineering,Biotechnology

Reference31 articles.

1. Some current directions in the theory and application of statistical process monitoring;Woodall;JQual Technol.,2014

2. Statistical process monitoring: basics and beyond;Qin;JChemom.,2003

3. The generalization of student's ratio;Hotelling;Ann Math Stat.,1931

4. A multivariate exponentially weighted moving average control chart;Lowry;Technometrics.,1992

5. Multivariate generalizations of cumulative sum quality-control schemes;Crosier;Technometrics.,1988

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