Paper Title:
Fault Detection in Tennessee Eastman Process Using Fisher’s Discriminant Analysis and Principal Component Analysis Modified by Genetic Algorithm
  Abstract

This paper describes hybrid multivariate methods: Fisher’s Discriminant Analysis and Principal Component Analysis improved by Genetic Algorithm. These methods are good techniques that have been used to detect faults during the operation of industrial processes. In this study, score and residual space of modified PCA and modified FDA are applied to the Tennessee Eastman Process simulator and show that modified PCA and modified FDA are more proficient than PCA and FDA for detecting faults.

  Info
Periodical
Chapter
Chapter 22: Metrology and Measurement
Edited by
Wu Fan
Pages
4255-4262
DOI
10.4028/www.scientific.net/AMM.110-116.4255
Citation
M. N. Nashalji, S. M. Razeghi, M. A. Shoorehdeli, M. Teshnehlab, "Fault Detection in Tennessee Eastman Process Using Fisher’s Discriminant Analysis and Principal Component Analysis Modified by Genetic Algorithm", Applied Mechanics and Materials, Vols. 110-116, pp. 4255-4262, 2012
Online since
October 2011
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