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
收藏
页码：15 / 20
页数：6
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