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

Mohamed-Faouzi Harkat Salah Djelel Noureddine Doghmane Mohamed Benouaret

Mohamed-Faouzi Harkat, Salah Djelel, Noureddine Doghmane, Mohamed Benouaret. Sensor Fault Detection, Isolation and Reconstruction Using Nonlinear Principal Component Analysis[J]. 国际自动化与计算杂志(英)/International Journal of Automation and Computing, 2007, 4(2): 149-155. doi: 10.1007/s11633-007-0149-6
引用本文: Mohamed-Faouzi Harkat, Salah Djelel, Noureddine Doghmane, Mohamed Benouaret. Sensor Fault Detection, Isolation and Reconstruction Using Nonlinear Principal Component Analysis[J]. 国际自动化与计算杂志(英)/International Journal of Automation and Computing, 2007, 4(2): 149-155. doi: 10.1007/s11633-007-0149-6
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

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

doi: 10.1007/s11633-007-0149-6

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

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出版历程
  • 收稿日期:  2006-02-13
  • 修回日期:  2006-12-15
  • 刊出日期:  2007-04-15

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

doi: 10.1007/s11633-007-0149-6

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.

English Abstract

Mohamed-Faouzi Harkat, Salah Djelel, Noureddine Doghmane, Mohamed Benouaret. Sensor Fault Detection, Isolation and Reconstruction Using Nonlinear Principal Component Analysis[J]. 国际自动化与计算杂志(英)/International Journal of Automation and Computing, 2007, 4(2): 149-155. doi: 10.1007/s11633-007-0149-6
引用本文: Mohamed-Faouzi Harkat, Salah Djelel, Noureddine Doghmane, Mohamed Benouaret. Sensor Fault Detection, Isolation and Reconstruction Using Nonlinear Principal Component Analysis[J]. 国际自动化与计算杂志(英)/International Journal of Automation and Computing, 2007, 4(2): 149-155. doi: 10.1007/s11633-007-0149-6
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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