Nonlinear PLS-application to fault detection

被引：0

作者：

Wilson, DJH ^{[1
]}

Irwin, GW ^{[1
]}

Lightbody, G ^{[1
]}

机构：

[1] Queens Univ Belfast, Dept Elect & Elect Engn, Adv Control Engn Res Grp, Belfast BT9 5AH, Antrim, North Ireland

来源：

(SAFEPROCESS'97): FAULT DETECTION, SUPERVISION AND SAFETY FOR TECHNICAL PROCESSES 1997, VOLS 1-3 | 1998年

关键词：

neural networks; statistical inference; fault detection; distillation columns;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A new approach to nonlinear Partial Least Squares (PLS) modelling using Radial Basis Function (RBF) neural networks to provide a nonlinear inner relationship is described, along with a novel technique (the hybrid BFGS algorithm) for training the networks. Results are given showing a significant improvement in modelling performance over linear PLS. In simulation tests, the technique is shown to be capable of detecting a variety of different faults on a validated model of an industrial distillation plant. Copyright (C) 1998 IFAC.

引用

页码：15 / 20

页数：6

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