Identification of the fragmentation of brittle particles during compaction process by the acoustic emission technique

Favretto-Cristini, Nathalie; Hégron, Lise; Sornay, Philippe

doi:10.1016/j.ultras.2015.12.010

Cited by 9 publications

(3 citation statements)

References 31 publications

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“…When the number of experiments to process and then of hits to characterize gets large, clustering methods based on these AE parameters are used to classify them. Depending on the diversity of the source mechanisms associated with the hits (cracks, fractures, delaminations ...) and the type of material (nuclear fuel, zircaloy, inox...) very different types of AE parameters can be discriminant [Favretto-Cristini et al, 2016,Anastassopoulos and Philippidis, 1995, Ai et al, 2010, Keyvan and Nagaraj, 1996. This is the case for the nuclear environment of interest here.…”

Section: Introduction and Context Of The Studymentioning

confidence: 99%

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

et al. 2019

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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.

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Section: Introduction and Context Of The Studymentioning

confidence: 99%

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

et al. 2019

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“…Among them the acoustic emission (AE) technique is of major interest. It has the advantage of being simple to adapt to nuclearoriented purposes and allows a quasi-real-time monitoring of the experiment [1]- [5]. One goal of the AE testing is to process the AE signals in such a way that they can be associated with specific physical source mechanisms occurring in the tested structure or material.…”

Section: Introductionmentioning

confidence: 99%

Impact of the Test Device on the Behavior of the Acoustic Emission Signals: Contribution of the Numerical Modeling to Signal Processing

et al. 2018

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Abstract-In a context of nuclear safety experiment monitoring with the non destructive testing method of acoustic emission, we study the impact of the test device on the interpretation of the recorded physical signals by using spectral finite element modeling. The numerical results are validated by comparison with real acoustic emission data obtained from previous experiments. The results show that several parameters can have significant impacts on acoustic wave propagation and then on the interpretation of the physical signals. The potential position of the source mechanism, the positions of the receivers and the nature of the coolant fluid have to be taken into account in the definition a pre-processing strategy of the real acoustic emission signals. In order to show the relevance of such an approach, we use the results to propose an optimization of the positions of the acoustic emission sensors in order to reduce the estimation bias of the time-delay and then improve the localization of the source mechanisms.

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“…When the number of recorded signals to process becomes very important, classical data-mining methods based on these variables are used to classify them and then the physical source mechanism associated with each class is identified. However, depending on the diversity of source mechanisms (cracks, fractures, delaminations ...) and the type of material (nuclear fuel, zircaloy, inox...) very different types of variables constructed from the AE signals can be discriminant [4,3,2,9]. In the case of nuclear safety experiment which is of interest in this article, the test device is composed of several types of materials and interact with a very complex environment, leading to a difficulty to get enough discriminant variables for a very heterogeneous sample of source mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Contribution of Functional Approach to the Classification and the Identification of Acoustic Emission Source Mechanisms

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Cristini

Favretto-Cristini

et al. 2017

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Identification of the fragmentation of brittle particles during compaction process by the acoustic emission technique

Cited by 9 publications

References 31 publications

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

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

Impact of the Test Device on the Behavior of the Acoustic Emission Signals: Contribution of the Numerical Modeling to Signal Processing

Contribution of Functional Approach to the Classification and the Identification of Acoustic Emission Source Mechanisms

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