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PLS modelling and fault detection on the Tennessee Eastman benchmark

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2 Author(s)
Wilson, D.J.H. ; Dept. of Electr. & Electron. Eng., Queen''s Univ., Belfast, UK ; Irwin, G.W.

This paper describes the application of multivariate regression techniques to the Tennessee Eastman benchmark process. Two methods are applied: linear partial least squares, and a nonlinear variant of this procedure using a radial basis function inner relation. These methods are used to create online inferential models of delayed process measurement. The redundancy so obtained is then used to generate a fault detection and isolation scheme for these sensors. The effectiveness of this scheme is demonstrated on a number of test faults

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American Control Conference, 1999. Proceedings of the 1999  (Volume:6 )

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