Premium
A kernel‐based approach for fault diagnosis in batch processes
Author(s) -
Vitale R.,
Noord O. E.,
Ferrer A.
Publication year - 2014
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2629
Subject(s) - kernel (algebra) , bilinear interpolation , linear discriminant analysis , computer science , projection (relational algebra) , discriminant , pattern recognition (psychology) , artificial intelligence , kernel fisher discriminant analysis , identification (biology) , kernel method , partial least squares regression , algorithm , mathematics , machine learning , support vector machine , computer vision , botany , combinatorics , biology
This article explores the potential of kernel‐based techniques for discriminating on‐specification and off‐specification batch runs, combining kernel‐partial least squares discriminant analysis and three common approaches to analyze batch data by means of bilinear models: landmark features extraction, batchwise unfolding, and variablewise unfolding. Gower's idea of pseudo‐sample projection is exploited to recover the contribution of the initial variables to the final model and visualize those having the highest discriminant power. The results show that the proposed approach provides an efficient fault discrimination and enables a correct identification of the discriminant variables in the considered case studies. Copyright © 2014 John Wiley & Sons, Ltd.