A generalizable deep learning framework for localizing and characterizing acoustic emission sources in riveted metallic panels
Open accessThe claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Stacking self-encoders with progressive compression of input AE waveform implemented AE source localization capable of localizing AE sources at arbitrary locations on the board.
“Ebrahimkhanlou et al. [80] implemented AE source localization by stacking self-encoders with progressive compression of the input AE waveform. Coordinate localization or area localization was performed through regression or softmax layers, capable of localizing AE sources at arbitrary locations on the board.”
From Deep Learning for Structural Health Monitoring: Data, Algorithms, Applications, Challenges, and Trends (2023) · cites this paper as [80]
Section 2 describes stacked autoencoders that progressively compress a wavelet-derived representation of AE waveforms; regression predicts source coordinates, while softmax classifies source zones. The paper reports coordinate-localization results for sources away from rivets, tested at grid locations (Section 3; Figure 10 caption). It does not establish coordinate localization at arbitrary locations across the entire panel, including near rivets. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe main applications of AE-based SHM are fatigue crack localization in metal plates, damage localization in composite panels, and concrete crack detection.
“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 [80]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
(2016) · Cited by 11,679 · see citing works
Pattern Recognition and Machine Learning
(2006) · doi:10.1007/978-0-387-45528-0 · Cited by 9,824 · see citing works
Extracting and composing robust features with denoising autoencoders
Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P.-A. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390294 · Cited by 7,436 · see citing works
A scaled conjugate gradient algorithm for fast supervised learning
Møller, M. F. (1993) · Neural Networks · doi:10.1016/s0893-6080(05)80056-5 · Cited by 4,094 · see citing works
Jia, F., Lei, Y., Lin, J., Zhou, X., & Lu, N. (2016) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2015.10.025 · Cited by 1,710 · see citing works
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 636 · see citing works
Deep Learning Based Approach for Bearing Fault Diagnosis
He, M., & He, D. (2017) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2017.2661250 · Cited by 487 · see citing works
Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals
Li, C., Sánchez, R., Zurita, G., Cerrada, M., Cabrera, D. R., & Vásquez, R. E. (2016) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2016.02.007 · Cited by 472 · see citing works
Kundu, T. (2013) · Ultrasonics · doi:10.1016/j.ultras.2013.06.009 · Cited by 402 · see citing works
Sarrafi, A., Mao, Z., Niezrecki, C., & Poozesh, P. (2018) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2018.01.050 · Cited by 288 · see citing works
Single-Sensor Acoustic Emission Source Localization in Plate-Like Structures Using Deep Learning
Ebrahimkhanlou, A., & Salamone, S. (2018) · Aerospace · doi:10.3390/aerospace5020050 · Cited by 171 · see citing works
Self-citationAl-Jumaili, S. K., Pearson, M. R., Holford, K. M., Eaton, M. J., & Pullin, R. (2015) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2015.11.026 · Cited by 154 · see citing works
Determination of impact location on composite stiffened panels
Khodaei, Z. S., Ghajari, M., & Aliabadi, M. H. (2012) · Smart Materials and Structures · doi:10.1088/0964-1726/21/10/105026 · Cited by 148 · see citing works
Ebrahimkhanlou, A., & Salamone, S. (2017) · Ultrasonics · doi:10.1016/j.ultras.2017.03.006 · Cited by 133 · see citing works
Self-citationHolford, K. M., Eaton, M. J., Hensman, J. J., et al. (2017) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2016.11.003 · Cited by 111 · see citing works
Damage localization in metallic plate structures using edge-reflected lamb waves
Ebrahimkhanlou, A., Dubuc, B., & Salamone, S. (2016) · Smart Materials and Structures · doi:10.1088/0964-1726/25/8/085035 · Cited by 99 · see citing works
Self-citationSen, N., & Kundu, T. (2018) · Ultrasonics · doi:10.1016/j.ultras.2018.01.011 · Cited by 78 · see citing works
Bhuiyan, M. Y., Bao, J., Poddar, B., & Victor Giurgiutiu, B. (2017) · Structural Health Monitoring · doi:10.1177/1475921717707356 · Cited by 72 · see citing works
Piezoelectric MEMS acoustic emission sensors
Kabir, M., Kazari, H., & Ozevin, D. (2018) · Sensors and Actuators A Physical · doi:10.1016/j.sna.2018.05.044 · Cited by 70 · see citing works
Ebrahimkhanlou, A., & Salamone, S. (2017) · Smart Materials and Structures · doi:10.1088/1361-665x/aa78de · Cited by 62 · see citing works
Self-citationMultimodal structural health monitoring based on active and passive sensing
Nasrollahi, A., Deng, W. Y., Ma, Z., & Rizzo, P. (2017) · Structural Health Monitoring · doi:10.1177/1475921717699375 · Cited by 59 · see citing works
Davoudi, R., Miller, G. R., & Kutz, J. N. (2018) · Automation in Construction · doi:10.1016/j.autcon.2018.09.024 · Cited by 57 · see citing works
Ciampa, F., & Meo, M. (2011) · The Journal of the Acoustical Society of America · doi:10.1121/1.3598458 · Cited by 50 · see citing works
Probabilistic Prognosis of Fatigue Crack Growth Using Acoustic Emission Data
Zárate, B. A., Caicedo, J. M., Yu, J., et al. (2012) · Journal of Engineering Mechanics · doi:10.1061/(asce)em.1943-7889.0000414 · Cited by 47 · see citing works
Ebrahimkhanlou, A., & Salamone, S. (2018) · Health Monitoring of Structural and Biological Systems XII · doi:10.1117/12.2296613 · Cited by 42 · see citing works
Self-citationDorafshan, S., Maguire, M., & Qi, X. (2016) · Digital Commons - USU (Utah State University) · doi:10.13140/rg.2.2.34024.47363 · Cited by 38 · see citing works
Mostavi, A., Kabir, M., & Ozevin, D. (2017) · Applied Physics Letters · doi:10.1063/1.5007771 · Cited by 33 · see citing works
Conditional classifiers and boosted conditional Gaussian mixture model for novelty detection
Mohammadi-Ghazi, R., Marzouk, Y., & Büyüköztürk, O. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.03.022 · Cited by 21 · see citing works
Probabilistic location estimation of acoustic emission sources in isotropic plates with one sensor
Ebrahimkhanlou, A., & Salamone, S. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2258618 · Cited by 12 · see citing works
Self-citationStructural operating deflection shape estimation via a hybrid computer-vision algorithm
Mao, Z., & Sarrafi, A. (2018) · doi:10.1117/12.2296784 · Cited by 8 · see citing works
Ebrahimkhanlou, A., & Salamone, S. (2017) · International Workshop on Structural Health Monitoring (IWSHM) · doi:10.12783/shm2017/14103 · Cited by 7 · see citing works
Self-citationMao, Z., Sarrafi, A., Niezrecki, C., et al. (2018) · doi:10.1117/12.2296688 · Cited by 7 · see citing works
Dubuc, B., Ebrahimkhanlou, A., & Salamone, S. (2017) · International Workshop on Structural Health Monitoring (IWSHM) · doi:10.12783/shm2017/14059 · Cited by 5 · see citing works
Self-citationEbrahimkhanlou, A., Dubuc, B., & Salamone, S. (2019) · SPIE Health Monitoring of Structural and Biological Systems · doi:10.1117/12.2514228 · Cited by 5 · see citing works
Self-citationPiezoelectric micromachined acoustic emission sensors for early stage damage detection in structures
Ozevin, D., Kabir, M., & Kazari, H. (2018) · doi:10.1117/12.2303465 · Cited by 1 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Jia, J., & Li, Y. (2023) · Sensors · doi:10.3390/s23218824 · Cited by 148 · see citing works
“The main applications are fatigue crack localization in metal plates [80,81,147], damage localization in composite panels [19], and concrete crack detection [62].”
BackgroundUncertainty-assisted deep vision structural health monitoring
Sajedi, S. O., & Liang, X. (2021) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12580 · Cited by 150 · see citing works
Capineri, L., & Bulletti, A. (2021) · doi:10.20944/preprints202103.0347.v2 · Cited by 131 · see citing works
Mariani, S., Rendu, Q., Urbani, M., & Sbarufatti, C. (2021) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2021.107748 · Cited by 86 · see citing works
Hesser, D. F., Mostafavi, S., Kocur, G. K., & Markert, B. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.04.108 · Cited by 82 · see citing works
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 · see citing works
Yang, L., & Xu, F. (2020) · IEEE Access · doi:10.1109/access.2020.3012521 · Cited by 32 · see citing works
- Authors
- Ebrahimkhanlou, A., Dubuc, B., & Salamone, S.
- Affiliations
- The University of Texas at Austin (US)
- Corresponding author
- Ebrahimkhanlou, Arvin
- Salamone, Salvatore
- Venue
- Mechanical Systems and Signal Processing
- Publisher
- Elsevier BV
- Published
- 2019-09
- Volume
- 130
- Pages
- 248-272
- ISSN
- 0888-3270 (ISSN-L)
- OpenAlex
- W2945697543
- Cited by
- 141
- References
- 36
- Citations by year
- 2026: 11 · 2025: 18 · 2024: 25 · 2023: 24 · 2022: 16 · 2021: 24 · 2020: 18 · 2019: 5
- FWCI
- 9.03
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Mechanical Systems and Signal Processing (acceptedVersion)
- Funders
- Office of Naval Research
- Topics
- Physical Sciences › Engineering › Mechanics of Materials › Ultrasonics and Acoustic Wave Propagation
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Engineering and Materials Analysis
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
- Keywords
- acoustic emission source localization, stacked autoencoder, structural health monitoring, plate structures, aluminum panels
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review