A multiobjective evolutionary approach to pattern recognition for robust diagnosis of process faults

被引：0
|
作者：
Marcu, T [1 ]
机构：
[1] Tech Univ Iasi, Dept Automat Control & Ind Informat, RO-6600 Iasi, Romania
来源：
(SAFEPROCESS'97): FAULT DETECTION, SUPERVISION AND SAFETY FOR TECHNICAL PROCESSES 1997, VOLS 1-3 | 1998年
关键词：
fault diagnosis; robustness; pattern recognition; process parameter estimation; non-parametric classifiers; multiobjective optimization; genetic algorithms;
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：
The problem of robust model-based diagnosis of process faults is addressed in the framework of pattern recognition. Evolutionary algorithms of genetic type are used to solve both problems of feature selection and classifier design by means of multiobjective optimization. Process coefficients are directly identified by an on-line procedure. Symptoms are then evaluated by a non-parametric classifier. Application to a laboratory process is included. A diagnosis subsystem is designed and implemented in real-time to detect incipient faults in the components of a three-tank system. Copyright (C) 1998 IFAC.
引用
收藏
页码：1183 / 1188
页数：6
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