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New fault detection method based on reduced kernel principal component analysis (RKPCA)

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Abstract

This paper proposes a new method for fault detection using a reduced kernel principal component analysis (RKPCA). The proposed RKPCA method consists on approximating the retained principal components given by the KPCA method by a set of observation vectors which point to the directions of the largest variances with the retained principal components. The proposed method has been tested on a chemical reactor and the results were satisfactory.

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Authors and Affiliations

  1. Laboratory of Automatic, Signal and Image Processing, Monastir, Tunisia

    Okba Taouali, Ines Jaffel, Hajer Lahdhiri & Hassani Messaoud

  2. Faculty of Engineering Annaba, Algérie Sciences, Department of Electronics, Univesity Badji Mokhtar, BP. 12, Annaba, 23000, Algeria

    Mohamed Faouzi Harkat

Authors
  1. Okba Taouali
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  2. Ines Jaffel
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  3. Hajer Lahdhiri
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  4. Mohamed Faouzi Harkat
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  5. Hassani Messaoud
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Correspondence to Okba Taouali.

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Taouali, O., Jaffel, I., Lahdhiri, H. et al. New fault detection method based on reduced kernel principal component analysis (RKPCA). Int J Adv Manuf Technol 85, 1547–1552 (2016). https://doi.org/10.1007/s00170-015-8059-1

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  • DOI: https://doi.org/10.1007/s00170-015-8059-1

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