A new fault classification approach applied to Tennessee Eastman benchmark process

摘要

This study presents a data-based methodology for fault detection and isolation in dynamic systems based on fuzzy/Bayesian approach for change point detection associated with a hybrid immuneeural formulation for pattern classification applied to the Tennessee Eastman benchmark process. The fault is detected when a change occurs in the signals from the sensors and classified into one of the classes by the immuneeural formulation. The change point detection system is based on fuzzy set theory associated with the MetropolisHastings algorithm and the classification system, the main contribution of this paper is based on a representation which combines the ClonALG algorithm with the Kohonen neural network. (C) 2016 Elsevier B.V. All rights reserved.