A signal processing approach for enhanced Acoustic Emission data analysis in high activity systems: Application to organic matrix composites

Structural elements made of Organic Matrix Composites (OMC) under complex loading may suffer from high Acoustic Emission (AE) activity caused by the emergence of different emission sources at high rates with high noise level, which finally engender continuous emissions. The detection of hits in this...

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Vydáno v:	Mechanical systems and signal processing Ročník 70-71; s. 1038 - 1055
Hlavní autoři:	Kharrat, M., Ramasso, E., Placet, V., Boubakar, M.L.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.03.2016 Elsevier
Témata:	Acoustic emission Automatic Control Engineering Carbon fiber reinforced plastics CFRP Computer Science Continuous AE signals Continuous fibers Damage Emergence Hit detection Mechanical systems Noise reduction Pattern recognition Quasi-static tests Signal processing Wavelet denoising Continuous AE signals Pattern recognition Hit detection CFRP Wavelet denoising Quasi-static tests
ISSN:	0888-3270, 1096-1216
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Abstract	Structural elements made of Organic Matrix Composites (OMC) under complex loading may suffer from high Acoustic Emission (AE) activity caused by the emergence of different emission sources at high rates with high noise level, which finally engender continuous emissions. The detection of hits in this situation becomes a challenge particularly during fatigue tests. This work suggests an approach based on the Discrete Wavelet Transform (DWT) denoising applied on signal segments. A particular attention is paid to the adjustment of the denoising parameters based on pencil lead breaks and their influence on the quality of the denoised AE signals. The validation of the proposed approach is performed on a ring-shaped Carbon Fiber Reinforced Plastics (CFRP) under in-service-like conditions involving continuous emissions with superimposed damage-related transients. It is demonstrated that errors in hit detection are greatly reduced leading to a better identification of the natural damage scenario based on AE signals. •The problem of continuous Acoustic Emission in CFRP composites was addressed.•A high loading rate test under a high noise level has fathered continuous emission.•The approach includes signal denoising, hit determination and feature extraction.•It is managed to eliminate the hits saturation using signal segmentation.•It allows a better identification of natural clusters using the GK algorithm.
AbstractList	Structural elements made of Organic Matrix Composites (OMC) under complex loading may suffer from high Acoustic Emission (AE) activity caused by the emergence of different emission sources at high rates with high noise level, which finally engender continuous emissions. The detection of hits in this situation becomes a challenge particularly during fatigue tests. This work suggests an approach based on the Discrete Wavelet Transform (DWT) denoising applied on signal segments. A particular attention is paid to the adjustment of the denoising parameters based on pencil lead breaks and their influence on the quality of the denoised AE signals. The validation of the proposed approach is performed on a ring-shaped Carbon Fiber Reinforced Plastics (CFRP) under in-service-like conditions involving continuous emissions with superimposed damage-related transients. It is demonstrated that errors in hit detection are greatly reduced leading to a better identification of the natural damage scenario based on AE signals. Structural elements made of Organic Matrix Composites (OMC) under complex loading may suffer from high Acoustic Emission (AE) activity caused by the emergence of different emission sources at high rates with high noise level, which finally engender continuous emissions. The detection of hits in this situation becomes a challenge particularly during fatigue tests. This work suggests an approach based on the Discrete Wavelet Transform (DWT) denoising applied on signal segments. A particular attention is paid to the adjustment of the denoising parameters based on pencil lead breaks and their influence on the quality of the denoised AE signals. The validation of the proposed approach is performed on a ring-shaped Carbon Fiber Reinforced Plastics (CFRP) under in-service-like conditions involving continuous emissions with superimposed damage-related transients. It is demonstrated that errors in hit detection are greatly reduced leading to a better identification of the natural damage scenario based on AE signals. •The problem of continuous Acoustic Emission in CFRP composites was addressed.•A high loading rate test under a high noise level has fathered continuous emission.•The approach includes signal denoising, hit determination and feature extraction.•It is managed to eliminate the hits saturation using signal segmentation.•It allows a better identification of natural clusters using the GK algorithm. In Acoustic Emission (AE) applications, the processing of continuous signals resulting from high AE rates or the superimposition of transients emitted from different emission sources, is a major problem. In complex systems like Organic Matrix Composites (OMC) fatigue tests, a high AE activity is produced due to the emergence of several emission sources. Such a kind of tests often involves a lot of noise that corrupts the original signal. Conventional threshold-based techniques are highly influenced by the noise level leading to erroneous hits detection. A suitable denoising method is thus necessary to process the signal before performing the hits detection and separation.This work deals with the processing of continuous AE signals obtained from Carbon Fiber Reinforced Plastics (CFRP) specimens under complex loading. When the size of each acquisition is large due to the sampling rate (generally from 2 to 5 MS/s), the signal is divided into short segments. The Discrete Wavelet Transform (DWT) is then used for signal denoising. An adjustment of its input parameters is achieved in order to improve the denoising process. A hit determination is thereafter performed in order to localize potential hits contained in each signal segment. By comparing the result obtained using the proposed approach to that of the usual threshold-based technique, we remark that the problem of erroneous hits is overcome. The performance of the proposed approach as well as the sensitivity to the denoising parameters are evaluated by studying the impact of errors in hit detection on feature extraction and damage assessment based on pattern recognition algorithms. The proposed approach ensures a better identification of natural clusters in AEs and improves the interpretation of damage mechanisms.
Author	Kharrat, M. Boubakar, M.L. Placet, V. Ramasso, E.
Author_xml	– sequence: 1 givenname: M. orcidid: 0000-0001-5272-1342 surname: Kharrat fullname: Kharrat, M. email: mohamed.kharrat@femto-st.fr organization: Department of Applied Mechanics, FEMTO-ST Institute – UMR CNRS 6174 – UFC/ENSMM/UTBM, 25000 Besançon, France – sequence: 2 givenname: E. surname: Ramasso fullname: Ramasso, E. organization: Department of Applied Mechanics, FEMTO-ST Institute – UMR CNRS 6174 – UFC/ENSMM/UTBM, 25000 Besançon, France – sequence: 3 givenname: V. surname: Placet fullname: Placet, V. organization: Department of Applied Mechanics, FEMTO-ST Institute – UMR CNRS 6174 – UFC/ENSMM/UTBM, 25000 Besançon, France – sequence: 4 givenname: M.L. surname: Boubakar fullname: Boubakar, M.L. organization: Department of Applied Mechanics, FEMTO-ST Institute – UMR CNRS 6174 – UFC/ENSMM/UTBM, 25000 Besançon, France
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Keywords	Continuous AE signals Pattern recognition Hit detection CFRP Wavelet denoising Quasi-static tests
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Snippet	Structural elements made of Organic Matrix Composites (OMC) under complex loading may suffer from high Acoustic Emission (AE) activity caused by the emergence... In Acoustic Emission (AE) applications, the processing of continuous signals resulting from high AE rates or the superimposition of transients emitted from...
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SubjectTerms	Acoustic emission Automatic Control Engineering Carbon fiber reinforced plastics CFRP Computer Science Continuous AE signals Continuous fibers Damage Emergence Hit detection Mechanical systems Noise reduction Pattern recognition Quasi-static tests Signal processing Wavelet denoising
Title	A signal processing approach for enhanced Acoustic Emission data analysis in high activity systems: Application to organic matrix composites
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