Support Vector Machines for fault detection

被引：0
|
作者：
Batur, C [1 ]
Zhou, L [1 ]
Chan, CC [1 ]
机构：
[1] Univ Akron, Dept Engn Mech, Akron, OH 44325 USA
来源：
PROCEEDINGS OF THE 41ST IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-4 | 2002年
关键词：
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：
Support Vector Machines (SVMs), based on Vapnik's statistical learning theory is a new tool that can be used for fault detection and isolation in dynamic systems. This paper presents a new approach that combines the system identification technique and the SVM learning algorithm for fault detection and isolation in dynamic systems. A conventional heat exchanger dynamics is used to illustrate the technique.
引用
收藏
页码：1355 / 1356
页数：2
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