Graphical enhancement to support PCA-based process monitoring and fault diagnosis.
Principal component analysis (PCA) for process modeling and multivariate statistical techniques for monitoring, fault detection, and diagnosis are becoming more common in published research, but are still underutilized in practice. This paper summarizes an in-depth case study on a chemical process with 20 monitored process variables, one of which reflects product quality. The analysis is performed using the PLS_Toolbox 2.01 with MATLAB, augmented with software which automates the analysis and implements a statistical enhancement that uses confidence limits on the residuals of each variable for fault detection rather than just confidence limits on an overall residual. The newly developed graphical interface identifies and displays each variable's contribution to the faulty behavior of the process; and it aids greatly in analyzing results. The case study analyzed within shows that using the statistical enhancement can reduce the fault detection time, and the automated graphical interface implements the enhancement easily.
Ralston
Patricia
P
Chemical Engineering Department, J. B. Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA. parals01@louisville.edu
DePuy
Gail
G
Graham
James H
JH
D001185:Artificial Intelligence; D003196:Computer Graphics; D003198:Computer Simulation; D004867:Equipment Design / Q000379:methods*; D004868:Equipment Failure; D019544:Equipment Failure Analysis; D004869:Equipment Safety / Q000379:methods*; D005246:Feedback; D015233:Models, Statistical; D009716:Numerical Analysis, Computer-Assisted; D025341:Principal Component Analysis; D014584:User-Computer Interface