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.
Similar content being viewed by others
References
Abraham C, Cornillon PA, Matzner-Løber E, Molinari N (2003) Unsupervised curve clustering using b-splines. Scand J Stat 30(3):581–595
Aguilera AM, Escabias M, Valderrama MJ (2006) Using principal components for estimating logistic regression with high-dimensional multicollinear data. Comput Stat Data Anal 50(8):1905–1924
Ai Q, Liu C, Chen X, He P, Wang Y (2010) Acoustic emission of fatigue crack in pressure pipe under cyclic pressure. Nucl Eng Des 240(10):3616–3620
Anastassopoulos AA, Philippidis TP (1995) Clustering methodology for the evaluation of acoustic emission from composites. J Acoust Emiss 13(1–2):11–22
Boudou A, Viguier-Pla S (2017) Commutator of projectors and of unitary operators. In: Functional statistics and related fields. Springer, pp 67–75
Cardot H, Ferraty F, Sarda P (1999) Functional linear model. Stat Probab Lett 45(1):11–22
Chiou JM, Müller HG, Wang JL (2004) Functional response models. Stat Sin 14:675–693
Cuesta-Albertos JA, Febrero-Bande M (2010) A simple multiway anova for functional data. Test 19(3):537–557
Cuevas A (2014) A partial overview of the theory of statistics with functional data. J Stat Plan Inference 147:1–23
Desgraupes B (2013) Clustering indices, vol 1. University of Paris Ouest-Lab Modal’X, Paris, p 34
Favretto-Cristini N, Hégron L, Sornay P (2016) Identification of the fragmentation of brittle particles during compaction process by the acoustic emission technique. Ultrasonics 67:178–189
Febrero-Bande M, González-Manteiga W, de la Fuente MO (2017) Variable selection in functional additive regression models. In: Functional statistics and related fields. Springer, pp 113–122
Ferraty F, Vieu P (2003) Curves discrimination: a nonparametric functional approach. Comput Stat Data Anal 44(1):161–173
Ferraty F, Vieu P (2006) Nonparametric functional data analysis: theory and practice. Springer, Berlin
Friedman J, Hastie T, Tibshirani R (2001) The elements of statistical learning. Springer Series in Statistics, vol 1. Springer, Berlin
Gautschi G (2002) Piezoelectric sensorics: force, strain, pressure, acceleration and acoustic emission sensors, materials and amplifiers. Springer, Berlin
Goia A, Vieu P (2016) An introduction to recent advances in high/infinite dimensional statistics. J Multivar Anal 46:1–6
Huckemann SF, Eltzner B (2017) Essentials of backward nested descriptors inference. In: Functional statistics and related fields. Springer, pp 137–144
Ieva F, Paganoni AM, Pigoli D, Vitelli V (2013) Multivariate functional clustering for the morphological analysis of electrocardiograph curves. J R Stat Soc Ser C (Appl Stat) 62(3):401–418
Jacques J, Preda C (2014) Functional data clustering: a survey. Adv Data Anal Classif 8(3):231–255
Jernkvist LO, Massih AR (2010) Nuclear fuel behavior under reactivity-initiated accident (ria) conditions. Technical report, Nuclear Energy Agency
Jiang Q, Meintanis SG, Zhu L (2017) Two-sample tests for multivariate functional data. In: Functional statistics and related fields. Springer, pp 145–154
Keyvan S, Nagaraj J (1996) Pattern recognition of acoustic signatures using art2: a neural network. J Acoust Emiss 14(2):97–102
Lila E, Aston JA, Sangalli LM (2017) Functional data analysis of neuroimaging signals associated with cerebral activity in the brain cortex. In: Functional statistics and related fields. Springer, pp 169–172
Murtagh F, Contreras P (2017) Algorithms for hierarchical clustering: an overview, II. Wiley Interdiscip Rev Data Min Knowl Discov. https://doi.org/10.1002/widm.1219
Pantera L, Traore OI (2015) Reproducible data processing research for the CABRI RIA experiments acoustic emission signal analysis. In: 4th International conference on advancements in nuclear instrumentation measurement methods and their applications (ANIMMA). IEEE, pp 1–8
Preda C, Saporta G, Lévéder C (2007) Pls classification of functional data. Comput Stat 22(2):223–235
R Development Core Team (2008) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0
Ramsay J, Silverman BW (2005) Functional data analysis. Springer, Berlin
Ramsay JO, Silverman BW (2002) Applied functional data analysis: methods and case studies. Springer, New York
Roget J (1988) Essais non destructifs: L’émission acoustique: Mise en œuvre et applications. CETIM, AFNOR
Rossi F, Conan-Guez B, El Golli, A (2004) Clustering functional data with the som algorithm. In: ESANN, pp 305–312
Rossi F, Villa N (2006) Support vector machine for functional data classification. Neurocomputing 69(7):730–742
Rudling P, Jernkvist LO, Garzarolli F, Adamson R, Mahmood T, Strasser A, Patterson C (2016) Nuclear fuel behaviour under ria conditions. Advanced Nuclear Technology International, Mölnlycke
Scrucca L (2004) qcc: an r package for quality control charting and statistical process control. R News 4(1):11–17
Signal Developers (2014) R Package signal: Signal processing. http://r-forge.r-project.org/projects/signal/
Sueur J, Aubin T, Simonis C (2008) Seewave: a free modular tool for sound analysis and synthesis. Bioacoustics 18:213–226
Traore OI, Cristini P, Favretto-Cristini N, Pantera L, Vieu P, Viguier-Pla S (2017a) Contribution of functional approach to the classification and the identification of acoustic emission source mechanisms. In: Functional statistics and related fields. Springer, pp 251–259
Traore OI, Favretto-Cristini N, Pantera L, Cristini P, Viguier-Pla S, Vieu P (2017b) Which methods and strategies to cope with noise complexity for an effective interpretation of acoustic emission signals in noisy nuclear environment? Acta Acust United Acust 103(6):903–916
Traore OI, Pantera L, Favretto-Cristini N, Cristini P, Viguier-Pla S, Vieu P (2017c) Structure analysis and denoising using singular spectrum analysis: application to acoustic emission signals from nuclear safety experiments. Measurement 104:78–88
Traore OI, Favretto-Cristini N, Cristini P, Pantera L, Viguier-Pla S (2018) Impact of the test device on acoustic emission signals from nuclear safety experiments: contribution of wave propagation modeling to signal processing. IEEE Trans Nucl Sci 65(9):2479–2489
Ullah S, Finch CF (2013) Applications of functional data analysis: a systematic review. BMC Med Res Methodol 13(1):43
Walders F, Liebl D (2017) Parameter regimes in partially functional linear regression for panel data. In: Functional statistics and related fields. Springer, pp 261–270
Wotzka D (2014) Mathematical model and regression analysis of acoustic emission signals generated by partial discharges. Appl Comput Math 3(5):225–230
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Traore, O.I., Cristini, P., Favretto-Cristini, N. et al. Clustering acoustic emission signals by mixing two stages dimension reduction and nonparametric approaches. Comput Stat 34, 631–652 (2019). https://doi.org/10.1007/s00180-018-00864-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00180-018-00864-w