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Clustering acoustic emission signals by mixing two stages dimension reduction and nonparametric approaches

Author

Listed:
  • O. I. Traore

    (Centrale Marseille, LMA)

  • P. Cristini

    (Centrale Marseille, LMA)

  • N. Favretto-Cristini

    (Centrale Marseille, LMA)

  • L. Pantera

    (CEA, DEN, DER/SRES, Cadarache)

  • P. Vieu

    (Université Paul Sabatier)

  • S. Viguier-Pla

    (Université Paul Sabatier
    Université de Perpignan via Domitia)

Abstract

In the context of nuclear safety experiments, we consider curves issued from acoustic emission. The aim of their analysis is the forecast of the physical phenomena associated with the behavior of the nuclear fuel. In order to cope with the complexity of the signals and the diversity of the potential source mechanisms, we experiment innovative clustering strategies which creates new curves, the envelope and the spectrum, from each raw hits, and combine spline smoothing methods with nonparametric functional and dimension reduction methods. The application of these strategies prove that in nuclear context, adapted functional methods are effective for data clustering.

Suggested Citation

  • O. I. Traore & P. Cristini & N. Favretto-Cristini & L. Pantera & P. Vieu & S. Viguier-Pla, 2019. "Clustering acoustic emission signals by mixing two stages dimension reduction and nonparametric approaches," Computational Statistics, Springer, vol. 34(2), pages 631-652, June.
    • Handle: RePEc:spr:compst:v:34:y:2019:i:2:d:10.1007_s00180-018-00864-w
    DOI: 10.1007/s00180-018-00864-w
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    References listed on IDEAS

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    Cited by:

    1. Germán Aneiros & Ricardo Cao & Philippe Vieu, 2019. "Editorial on the special issue on Functional Data Analysis and Related Topics," Computational Statistics, Springer, vol. 34(2), pages 447-450, June.
    2. Golovkine, Steven & Klutchnikoff, Nicolas & Patilea, Valentin, 2022. "Clustering multivariate functional data using unsupervised binary trees," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    3. Rhoden, Imke & Weller, Daniel & Voit, Ann-Katrin, 2021. "Spatio-temporal dynamics of European innovation: An exploratory approach via multivariate functional data cluster analysis," Ruhr Economic Papers 926, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    4. Amandine Schmutz & Julien Jacques & Charles Bouveyron & Laurence Chèze & Pauline Martin, 2020. "Clustering multivariate functional data in group-specific functional subspaces," Computational Statistics, Springer, vol. 35(3), pages 1101-1131, September.

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