PLS modelling and fault detection on the Tennessee Eastman benchmark

Abstract

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.