Title :
PLS modelling and fault detection on the Tennessee Eastman benchmark
Author :
Wilson, D.J.H. ; Irwin, G.W.
Author_Institution :
Dept. of Electr. & Electron. Eng., Queen´´s Univ., Belfast, UK
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
Keywords :
chemical industry; fault diagnosis; least squares approximations; principal component analysis; quality control; radial basis function networks; redundancy; statistical process control; Eastman Kodak plant; RBF neural networks; Tennessee Eastman benchmark; fault detection; fault diagnosis; inferential models; multivariate regression; partial least squares; principal component analysis; quality control; redundancy; sensors; statistical process control; Benchmark testing; Control engineering; Delay estimation; Electrical fault detection; Fault detection; Least squares methods; Multivariate regression; Principal component analysis; Process control; Software testing;
Conference_Titel :
American Control Conference, 1999. Proceedings of the 1999
Conference_Location :
San Diego, CA
Print_ISBN :
0-7803-4990-3
DOI :
10.1109/ACC.1999.786264
