Joint Sparse Principal Component Analysis Based Roust Sparse Fault Detection

被引：0

作者：

Jiang, Wenlan ^{[1
]}

Zhang, Tao ^{[1
]}

Wang, Huangang ^{[1
]}

机构：

[1] Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China

来源：

PROCEEDINGS OF 2020 IEEE 9TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE (DDCLS'20) | 2020年

关键词：

Fault detection; feature selection; Joint Sparse PCA([!text type='JS']JS[!/text]PCA); Tennessee Eastman process(TEP);

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：

In this paper, a novel variant of PCA, joint sparse principal component analysis(JSPCA), is adopted into robust sparse fault detection. By imposing l(2,1) norm jointly on the loss function and the regularization term of traditional sparse PCA, the JSPCA based fault detection method achieves sparse feature selection and robust fault detection simultaneously without high computation cost. The effectiveness of the proposed method is evaluated on the Tennessee Eastman process.

引用

页码：1234 / 1239

页数：6

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