Fault diagnosis of lead-zinc smelting furnace based on multi-class support vector machines

被引：0
|
作者：
Jiang, Shaohua [1 ]
Gui, Weihua [1 ]
Yang, Chunhua [1 ]
Xie, Yongfang [1 ]
机构：
[1] Cent S univ, Sch Informat Sci & Engn, Changsha, Hunan, Peoples R China
来源：
2007 IEEE INTERNATIONAL CONFERENCE ON CONTROL AND AUTOMATION, VOLS 1-7 | 2007年
关键词：
support vector machine (SVM); data reliability analysis; fault diagnosis; multi-class SVM classifier;
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 machine (SVM) is powerful for the problem with small sampling, nonlinear and high dimension. A multi-class SVM classifier is applied to fault diagnosis of Imperial Smelting Furnace in this paper. The input data is preprocessed through a special method based on the data reliability analysis technology, and six features are extracted as the input to multiple fault classifier for identify faults, which adapt an improved 'one to others' algorithm. The real application results show that the classifier has an excellent performance on training speed and reliability.
引用
收藏
页码：2231 / 2236
页数：6
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