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

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.

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