Multivariate statistical process monitoring and fault diagnosis based on an integration method of PCA-ICA and CSM

被引：3

作者：

Yang, Yinghua ^{[1
]}

Chen, Yonglu ^{[1
]}

Chen, Xiaobo ^{[1
]}

Qin, Shukai ^{[1
]}

机构：

[1] Northeastern Univ, Coll Informat Sci & Engn, Shenyang, Liaoning, Peoples R China

来源：

GREEN POWER, MATERIALS AND MANUFACTURING TECHNOLOGY AND APPLICATIONS, PTS 1 AND 2 | 2011年 / 84-85卷

关键词：

Fault Diagnosis; Principal Component Analysis; Independent Component Analysis; Continuous String Matching;

D O I：

10.4028/www.scientific.net/AMM.84-85.110

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In this paper, an approach for multivariate statistical process monitoring and fault diagnosis based on an improved independent component analysis (ICA) and continuous string matching (CSM) is presented, which can detect and diagnose process fault faster and with higher confidence level. The trial on the Tennessee Eastman process demonstrates that the proposed method can diagnose the fault effectively. Comparison of the method with the well established principal component analysis is also made.

引用

页码：110 / 114

页数：5

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