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Publication | Date |
Damage diagnostic and lifetime prognosis for ceramic matrix composite with acoustic emission during long-term mechanical tests at intermediate temperature. N. Godin 9, P. Reynaud, G. Fantozzi 2 National Institute of Applied Science ( INSA-Lyon)- University of Lyon 67, Villeurbanne, France Acoustic Emission (AE)
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Damage of composite materials is a key factor in the durability in service. It is therefore essential to define the most suitable damage indicators and to develop models to estimate the Remaining Useful Lifetime (RUL) from analysis of precursor events resulting from damage. Acoustic emission is relevant to the development of the PHM (Prognostic and Health Management) because it allows knowing the state of damage of a composite structure in real time. This work is dedicated to lifetime prediction using AE for long-term tests on CMC during static and cyclic fatigue tests. New indicators of damage have been defined, based mainly on acoustic energy analyses. In this context, an equivalent energy of AE sources is defined in order to eliminate effects of attenuation due to propagation distance. These indicators highlight critical times (around 50 % of the composite lifetime) allowing an evaluation of the remaining lifetime. A linear correlation is observed between these critical times and the lifetime duration. For a prognostic phase, the results obtained with this empirical law are compared to those obtained with a power law such as a Benioff law. Moreover, the clustering of acoustic emission, using a random forest approach, makes possible to identify the mechanism responsible for this critical time. For the static fatigue test, the critical time around 50 % correspond to the delayed failure of fibres. Moreover, the determination of the acoustic signature and characteristic time is linked to testing conditions and specimen geometries. Therefore, sizes effects are investigated with modelling works.
| EWGAE 2018 Session: Advanced Composites | 2019-01 |
Modelling of fiber break as Acoustic Emission Source in SFFT: comparison with experimental results Z. Hamam 2, N. Godin 9, C. Fusco 2, T. Monnier 8 National Institute of Applied Science ( INSA-Lyon)- University of Lyon 67, Villeurbanne, France Acoustic Emission (AE)
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The objective of this work is to build a quantitative relationship between the fiber break as source of Acoustic Emission (AE) and the detected signal by unravelling the effect of each stage of the AE acquisition chain. For this purpose, an AE modelling is carried out using the Finite Element Method and then the simulation is compared to experimental results of Single Fiber Fragmentation Test (SFFT).
The SFFT is used in order to produce preferential fiber break. It is carried out on dogbone shape specimens made from epoxy/amine matrix and a long carbon fiber T700 embedded in this resin. Two different types of transducer are used in order to gather information on a wider frequency bandwidth. The analysis of detected signals shows an important dependency of distance between transducer and source on the frequency content of signals. In this case, high frequency content for the signal associated to fiber breakage is not validated for all signals.
For the modeling part, the entire geometry of the specimen is modelled. The geometry is subjected to a tensile loading. The fiber breakage occurs by separating the nodes forming fracture faces. The numerical out-of-plane velocities are collected on the specimen surface. The sensor is taken into account by its transfer function, which is experimentally determined by the reciprocity method. After being validated, the FE model is used to study the effects of different parameters on the signal, such as specimen geometry, the propagation medium and the location of the failure
| EWGAE 2018 Session: Advanced Composites | 2019-01 |
Signal-level clustering of acoustic emission streaming P. Butaud1 2, E. Ramasso1 8, H. Ahayan1, T. Jeannin1, N. Godin2 9, V. Placet1 7 1aDep. of Applied Mechanics bDepartment of Applied Mechanics; Institut FEMTO-ST 11, Besançon, France 2National Institute of Applied Science ( INSA-Lyon)- University of Lyon 67, Villeurbanne, France Acoustic emission, Composite materials, Data processing, Vibration analysis and testing
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The Acoustic Emission (AE) technique is widely used for material characterization and structural health monitoring. Once collected, the standard approach to process and interpret the potentially large amount of AE streaming data is generally based on four main phases: 1) wave picking, 2) feature extraction, 3) clustering, 4) evaluation of clusters with, if possible, correlation with AE sources.
The first phase, wave picking, isolates relevant AE signals from noise within the streaming. It is based on a set of thresholds to ensure that AE signals have a sufficiently high amplitude during a certain amount of time. In the second phase, features are extracted from AE signals, where the goal is mainly to represent AE signals in the same dimensional space. Indeed, AE signals have generally different lengths which cannot be managed by standard clustering methods. Clustering aims at finding out the data structure to infer the behavior of the material during solicitations. Evaluation is finally focused on the optimization of the parameters (best subset of features and optimal number of clusters) according to some criteria.
In this paper, we propose to work at the signal (AE waveform) level, in an unsupervised manner. While the supervised case has already been proposed in the past, to the author’s best of knowledge, unsupervised learning at the signal (or streaming) level for AE interpretation is a novel approach. The proposed method has three main advantages: 1) it can be used for supervised or unsupervised learning; 2) it can work directly on streaming without wave picking. This particularly simplifies the processing by reducing the number of algorithms and parameters to be used; 3) if wave picking is used to extract AE hits, then the method does not require feature extraction from AE signals. It simplifies the processing by avoiding the heavy task of feature selection.
The proposed method relies on a statistical model of the temporal evolution of the streaming data. This model is preliminary built using previous data or physics-based knowledge. The model is then applied online and generates a set of similarity measures which can then be used in clustering to get the data structure. The influence of the choice of the model is alleviated by bootstrapped ensembles and clustering fusion. The method has connections with a well-known approach in SHM called anomaly detection, which will be discussed. Illustrations are provided for bio-sourced composite materials.
| EWSHM 2018 Session: Acoustic Emission | 2018-11 |
Classification of acoustic emission signals using wavelets and Random Forests: application to localized corrosion N. Morizet , N. Godin 9, J. Tang, M. Fregonese 2, B. Normand National Institute of Applied Science ( INSA-Lyon)- University of Lyon 67, Villeurbanne, France
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This work aims at proposing a novel approach to classify acoustic emission (AE) signals deriving
from corrosion damage, even if embedded into a noisy environment. Tests involving noise and crevice corrosion
are conducted, by preprocessing the waveforms including wavelet denoising and extracting a rich set of features as
input of the Random Forest algorithm. To this end, a software called RF-CAM has been developed. Results show
this approach is very efficient on ground truth data and is also very promising on real data, especially for its
reliability, performance and speed, which are serious criteria for the chemical industry.
| Journal-AE Session: Volume 34, 2016 | 2017-10 |
Acoustic Emission modeling from the source to the detected signal: model validation and identification of relevant descriptors T. Gall , N. Godin 9, T. Monnier 8, C. Fusco 2, Z. Hamam 2 National Institute of Applied Science ( INSA-Lyon)- University of Lyon 67, Villeurbanne, France
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The interpretation of data measured by Acoustic Emission (AE) is largely based on empirical
correlations between the respective characteristics of the source and the measured signal. The main limitation is that
changes due to the acquisition chain are not well known. Therefore, the aim of this work is to build a quantitative
relationship between the AE sources and the detected signal by unravelling the effect of the different stages of the
acquisition chain. For this purpose, an AE modelling, in which each part is considered individually, is carried out.
This will serve to understand the effects of different parameters on the signal waveform, such as the type of damage,
the geometry of the specimen and the effect of the piezoelectric sensor.
| Journal-AE Session: Volume 34, 2016 | 2017-10 |
Acoustic emission and damage evolution at mean temperature under air of a SiC/[Si-B-C] composite subjected to cyclic and static loading : towards lifetime prediction E. Racie, N. Godin 9, P. Reynard, M. R'Milli, G. Fantozzi 2 National Institute of Applied Science ( INSA-Lyon)- University of Lyon 67, Villeurbanne, France
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The low density and the high tensile strength of Ceramic Matrix Composites (CMC) make them a
good technical solution to design aeronautical structural components. To fully understand damage mechanisms
and be able to design components, its behavior has to be analyzed during fatigue tests. The aim of the present
study is to consider the possibility of predicting rupture time from acoustic emission monitoring. New indicators
of damage are defined, based on acoustic energy. These indicators highlight critical times or characteristic times
allowing an evaluation of the remaining lifetime.
| Journal-AE Session: Volume 34, 2016 | 2017-10 |
Primary Calibration of Acoustic Emission Sensors by the Method of Reciprocity, Theoretical and Experimental Considerations T. Monnier1 8, S. Dia1 2, N. Godin1 9, F. Zhang2 25 1National Institute of Applied Science ( INSA-Lyon)- University of Lyon 67, Villeurbanne, France 2Département Equipements sous pression et Ingénierie de lInstrumentation (EPI); French Industrial and Mechanical Technical Centre (CETIM) 92, Senlis, France
| Journal-AE Session: Volume 30, 2012 | 2014-03 |
Primary Calibration of Acoustic Emission Sensors by the Method of Reciprocity, Theoretical and Experimental Considerations S. Dia1 2, T. Monnier1 8, N. Godin1 9, F. Zhang2 25 1National Institute of Applied Science ( INSA-Lyon)- University of Lyon 67, Villeurbanne, France 2French Industrial and Mechanical Technical Centre (CETIM) 92, Senlis, France calibration, AE sensor, Acoustic Emission (AE),
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This paper is focused on the calibration of acoustic emission sensors by means of the reciprocity method. Therefore, the problem of the reciprocity criterion in the case of ultrasonic waves in solids is addressed. This latter criterion, upon which the reciprocity method is based, is well established and widely used in the case of waves in fluid medium (air, water etc.). However, and despite that the first papers have been published forty years ago, its application in the case of ultrasonic waves in solids (Rayleigh waves, longitudinal and transversal waves and Lamb waves) now raises a number of discussions in the community of acoustic emission. Therefore, calibration of acoustic emission sensors by means of reciprocity method starts anew to be controversial. After a review of the concept of reciprocity since its origin in electromagnetism to its application to electro-acoustic transducers, a critical analysis of previous theoretical work on the proof of the validity of the reciprocity theorem in the case of ultrasonic waves in solid will be done. An approach for calculating the coefficient of reciprocity by finite element method and an attempt to experimentally check its validity by means of impedance measurement of acoustic emission sensors will be presented. Finally, a presentation will be given on our recent works to extract an aperture function which is closer to the real behavior of sensor and to take it into account in the calibration process. This work is financially supported by the ANR, contract n° ANR-06-CIS6-01.
| EWGAE 2012 Session: AE Sensors and Transducers | 2012-11 |
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