Oumar I. Traoré, Paul Cristini, Nathalie
Favretto-Cristini,
Laurent Pantera, Philippe Vieu, Sylvie Viguier-Pla (2019)
Clustering acoustic emission signals by mixing two stages
dimension reduction and nonparametric approaches.
Comput. Stat. 34 631-652.
- Classification
- 1.01490, 1.05090, 1.04200
- Keywords
- Functional clustering, Curve smoothing,
Hierarchical clustering, Semi-metric, Functional Principal
Components Analysis
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 create 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.