Sensor Fault Detection, Isolation and Reconstruction Using Nonlinear Principal Component Analysis

被引：33

作者：

Harkat, Mohamed-Faouzi ^{[1
]}

Djelel, Salah ^{[1
]}

Doghmane, Noureddine ^{[1
]}

Benouaret, Mohamed ^{[1
]}

机构：

[1] Univ Badji Mokhtar Annaba, Dept Elect, Fac Sci Ingn, BP 12, Annaba 23000, Algeria

来源：

INTERNATIONAL JOURNAL OF AUTOMATION AND COMPUTING | 2007年 / 4卷 / 02期

关键词：

Fault detection and isolation; reconstruction; nonlinear PCA (NLPCA); neural networks;

D O I：

10.1007/s11633-007-0149-6

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

State reconstruction approach is very useful for sensor fault isolation, reconstruction of faulty measurement and the determination of the number of components retained in the principal components analysis (PCA) model. An extension of this approach based on a Nonlinear PCA (NLPCA) model is described in this paper. The NLPCA model is obtained using five layer neural network. A simulation example is given to show the performances of the proposed approach.

引用

页码：149 / 155

页数：7

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