Volume 4 Number 2
April 2007
Article Contents
Mohamed-Faouzi Harkat, Salah Djelel, Noureddine Doghmane and Mohamed Benouaret. Sensor Fault Detection, Isolation and Reconstruction Using Nonlinear Principal Component Analysis. International Journal of Automation and Computing, vol. 4, no. 2, pp. 149-155, 2007. doi: 10.1007/s11633-007-0149-6
Cite as: Mohamed-Faouzi Harkat, Salah Djelel, Noureddine Doghmane and Mohamed Benouaret. Sensor Fault Detection, Isolation and Reconstruction Using Nonlinear Principal Component Analysis. International Journal of Automation and Computing, vol. 4, no. 2, pp. 149-155, 2007. doi: 10.1007/s11633-007-0149-6

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

  • Received: 2006-02-13
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

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

Abstract: 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.

Mohamed-Faouzi Harkat, Salah Djelel, Noureddine Doghmane and Mohamed Benouaret. Sensor Fault Detection, Isolation and Reconstruction Using Nonlinear Principal Component Analysis. International Journal of Automation and Computing, vol. 4, no. 2, pp. 149-155, 2007. doi: 10.1007/s11633-007-0149-6
Citation: Mohamed-Faouzi Harkat, Salah Djelel, Noureddine Doghmane and Mohamed Benouaret. Sensor Fault Detection, Isolation and Reconstruction Using Nonlinear Principal Component Analysis. International Journal of Automation and Computing, vol. 4, no. 2, pp. 149-155, 2007. doi: 10.1007/s11633-007-0149-6
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