Sensor fault detection, isolation and reconstruction using nonlinear principal component analysis

Mohamed Faouzi Harkat, Salah Djelel, Noureddine Doghmane, Mohamed Benouaret

Research output: Contribution to journalArticle

35 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)149-155
Number of pages7
JournalInternational Journal of Automation and Computing
Issue number2
Publication statusPublished - 1 Apr 2007



  • Fault detection and isolation
  • Neural networks
  • Nonlinear PCA (NLPCA)
  • Reconstruction

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modelling and Simulation
  • Computer Science Applications
  • Applied Mathematics

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