A Hybrid Methodology For On-Line Process Monitoring

被引：0

作者：

Kumar, Rakesh ^{[1
]}

Jade, Avinash M. ^{[1
]}

Jayaraman, Valadi K. ^{[1
]}

Kulkarni, Bhaskar D. ^{[1
]}

机构：

[1] Natl Chem Lab, Div Chem Engn, Pune, Maharashtra, India

来源：

INTERNATIONAL JOURNAL OF CHEMICAL REACTOR ENGINEERING | 2004年 / 2卷

关键词：

Process monitoring; locally linear embedding; abnormality detection; support vector domain distribution; novelty detection; single class classification;

D O I：

暂无

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

A hybrid strategy of using (i) locally linear embedding for nonlinear dimensionality reduction of high dimensional data and (ii) support vector machines for classification of the resultant features is proposed as a robust methodology for process monitoring. Illustrative examples substantiate the methodology vis-a-vis current practice.

引用

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页数：20

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