The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Acoustic emission data based deep learning approach for classification and detection of damage-sources in a composite panel
Open accessAbstract
Structural health monitoring for lightweight complex composite structures is being investigated in this paper with a data-driven deep learning approach to facilitate automated learning of the map of transformed signal features to damage classes. Towards this, a series of acoustic emission (AE) based laboratory experiments have been carried out on a composite sample using a piezoelectric AE sensor network. The registered time-domain AE signals from the assigned sensor networks on the composite panel are processed with the continuous wavelet transform to extract time-frequency scalograms. A convolutional neural network based deep learning architecture is proposed to automatically extract the discrete damage features from the scalogram images and use them to classify damage-source regions in the composite panel. The proposed deep-learning approach has shown an effective damage monitoring potential with high training, validation and test accuracy for unseen datasets as well as for entirely new neighboring damage datasets. Further, the proposed network is trained, validated and tested only for the peak-signal data extracted from the raw AE data. The application of peak-signal scalogram data has shown a significant improvement in damage-source classification performance with high training, validation and test accuracy.
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“Currently, the most commonly used SHM methods are vibration-based SHM [14,15,16,17,18], AE-based SHM [9,19,20], Guided Wave (GW) based SHM [21,22,23,24,25], and Electro-Mechanical Impedance (EMI) based SHM [26,27].”
From Deep Learning for Structural Health Monitoring: Data, Algorithms, Applications, Challenges, and Trends (2023) · cites this paper as [19]
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In the citing paper
“AE-based SHM is a passive, non-destructive testing method for analyzing structural damage characteristics after converting AE signals into electrical waveforms [19].”
From Deep Learning for Structural Health Monitoring: Data, Algorithms, Applications, Challenges, and Trends (2023) · cites this paper as [19]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“The main applications are fatigue crack localization in metal plates [80,81,147], damage localization in composite panels [19], and concrete crack detection [62].”
From Deep Learning for Structural Health Monitoring: Data, Algorithms, Applications, Challenges, and Trends (2023) · cites this paper as [19]
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Citations
Cites
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077
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Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018) · Insights into Imaging · doi:10.1007/s13244-018-0639-9 · Cited by 4,682 · cited 3×
“The proposed CNN architecture shows 6-different layers and the functions of these layers are briefly explained here, however, a detailed description can be found in [40-42].”
BackgroundMethodCaffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. J. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.5093 · Cited by 3,752
Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks
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(2009) · Wiley eBooks · doi:10.1002/9780470061626 · Cited by 778
Research works printed but not linked: 3
- Ono, 2012, Research and application of AE on advanced composite, J Acoust Emiss, 30, 180
- Unnthorsson, 2008, Acoustic emission based failure criterion for CFRP, Int J Fatig, 30, 11, 10.1016/j.ijfatigue.2007.02.024 · DOI
- Dunegan, 2000, An alternative to pencil lead breaks for simulation of acoustic emission signal sources, The DECI report
Cited by
Jia, J., & Li, Y. (2023) · Sensors · doi:10.3390/s23218824 · Cited by 182
Liao, Y., Qing, X., Wang, Y., & Zhang, F. (2023) · Composite Structures · doi:10.1016/j.compstruct.2023.116871 · Cited by 73
Guo, S., Ding, H., Li, Y., Feng, H., Xiong, X., Su, Z., & Feng, W. (2022) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2022.109508 · Cited by 43
Metadata
- Authors
- Sikdar, S., Liu, D., & Kundu, A.
- Affiliations
- Ghent University (BE)
- University of East Anglia (GB)
- Cardiff University (GB)
- Corresponding author
- Sikdar, Shirsendu
- Venue
- Composites Part B: Engineering
- Publisher
- Elsevier BV
- Published
- 2022-01
- Volume
- 228
- Pages
- 109450
- ISSN
- 1359-8368 (ISSN-L)
- OpenAlex
- W3209124788
- Cited by
- 188
- References
- 40
- Citations by year
- 2026: 45 · 2025: 52 · 2024: 37 · 2023: 34 · 2022: 20
- FWCI
- 11.13
- Type
- journal article
- Language
- en
- Open access
- green
- ueaeprints.uea.ac.uk
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Composites Part B Engineering (publishedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (acceptedVersion)
- UEA Digital Repository (University of East Anglia) (submittedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (submittedVersion)
- Funders
- Royal Academy of Engineering
- Fonds Wetenschappelijk Onderzoek
- Vlaamse regering
- Engineering and Physical Sciences Research Council (EP/V055577/1)
- Topics
- Physical Sciences › Engineering › Mechanics of Materials › Ultrasonics and Acoustic Wave Propagation
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Engineering and Materials Analysis
- Keywords
- acoustic emission, composite panels, damage detection, damage classification, structural health monitoring, continuous wavelet transform, scalogram, convolutional neural networks, piezoelectric sensor networks, lightweight composite structures
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- This page
- CoChat. (2026). Citation check: Acoustic emission data based deep learning approach for classification and detection of damage-sources in a composite panel. https://app.cochat.ai/papers/10.1016/j.compositesb.2021.109450
- The paper
- Sikdar, S., Liu, D., & Kundu, A. (2022). Acoustic emission data based deep learning approach for classification and detection of damage-sources in a composite panel. Composites Part B: Engineering. https://doi.org/10.1016/j.compositesb.2021.109450
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
