Variable contribution identification and visualization in multivariate statistical process monitoring
Date
2020-01
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
Multivariate statistical process monitoring (MSPM) has received book-length treatments and wide spread application in industry. In MSPM, multivariate data analysis techniques such as principal component analysis (PCA) are commonly employed to project the (possibly many) process variables onto a lower dimensional space where they are jointly monitored given a historical or specified reference set that is within statistical control. In this paper, PCA and biplots are employed together in an innovative way to develop an efficient multivariate process monitoring methodology for variable contribution identification and visualization. The methodology is applied to a commercial coal gasification production facility with multiple parallel production processes. More specifically, it is shown how the methodology is used to specify the optimal principal component combinations and biplot axes for visualization and interpretation of process performance, and for the identification of the critical variables responsible for performance deviations, which yielded direct benefits for the commercial production facility.
Description
CITATION: Rossouw, R. F.; Coetzer, R. L. J. & Le Roux, N. J. 2020. Variable contribution identification and visualization in multivariate statistical process monitoring. Chemometrics and Intelligent Laboratory Systems, 198. doi:10.1016/j.chemolab.2019.103894
The original publication is available at https://www.sciencedirect.com/journal/chemometrics-and-intelligent-laboratory-systems
The original publication is available at https://www.sciencedirect.com/journal/chemometrics-and-intelligent-laboratory-systems
Keywords
Multivariate analysis -- Graphic methods, Graphical modeling (Statistics), Statistics -- Graphic methods
Citation
Rossouw, R. F.; Coetzer, R. L. J. & Le Roux, N. J. 2020. Variable contribution identification and visualization in multivariate statistical process monitoring. Chemometrics and Intelligent Laboratory Systems, 198. doi:10.1016/j.chemolab.2019.103894