Locating and imaging composite damage based on frequency spectrum detection of lamb waves Article Swipe
YOU?
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· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fphy.2022.1073206
Lamb wave is widely concerned in damage detection of composite structures due to its long propagation distance and strong sensitivity to damage. In order to solve the problem of damage information extraction, high-precision imaging positioning of damage is realized. A damage location imaging method based on lamb signal spectrum is proposed. The damage location imaging of composite materials is realized by simulation and experiment respectively. Firstly, the feasibility of the algorithm is verified by simulation. The circular sensor array is arranged in the structure. Each sensor acts as an actuator and rotates clockwise to generate Lamb waves, while other sensors collect signals. The frequency spectrum of the signal is extracted to calculate the damage factor, and the probability imaging algorithm is used to realize the single damage and multiple damage location imaging. Then, the damage imaging localization is realized through experiments. Finally, the resistant noise characteristic of the proposed algorithm is analyzed. The results show that both single damage and multiple damage can be located in strong noise environment. It shows that this method can realize damage imaging directly without preprocessing the original signal. It has great potential in damage location imaging under strong noise environment.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fphy.2022.1073206
- https://www.frontiersin.org/articles/10.3389/fphy.2022.1073206/pdf
- OA Status
- gold
- Cited By
- 6
- References
- 26
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4308937349
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4308937349Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fphy.2022.1073206Digital Object Identifier
- Title
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Locating and imaging composite damage based on frequency spectrum detection of lamb wavesWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-11-11Full publication date if available
- Authors
-
Wenchao Zhang, Chenhui Su, Yuhang Zhang, Hongjuan YangList of authors in order
- Landing page
-
https://doi.org/10.3389/fphy.2022.1073206Publisher landing page
- PDF URL
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https://www.frontiersin.org/articles/10.3389/fphy.2022.1073206/pdfDirect link to full text PDF
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-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.frontiersin.org/articles/10.3389/fphy.2022.1073206/pdfDirect OA link when available
- Concepts
-
Lamb waves, SIGNAL (programming language), Noise (video), Acoustics, Preprocessor, Computer science, Physics, Optics, Wave propagation, Artificial intelligence, Image (mathematics), Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 2, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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26Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.on | 45 |
| abstract_inverted_index.to | 12, 20, 24, 93, 110, 122 |
| abstract_inverted_index.The | 51, 75, 102, 152 |
| abstract_inverted_index.and | 17, 62, 90, 115, 127, 159 |
| abstract_inverted_index.can | 162, 174 |
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| abstract_inverted_index.its | 13 |
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| abstract_inverted_index.Each | 84 |
| abstract_inverted_index.Lamb | 0, 95 |
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| abstract_inverted_index.that | 155, 171 |
| abstract_inverted_index.this | 172 |
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| abstract_inverted_index.wave | 1 |
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| abstract_inverted_index.array | 78 |
| abstract_inverted_index.based | 44 |
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| abstract_inverted_index.noise | 144, 167, 194 |
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| abstract_inverted_index.other | 98 |
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| abstract_inverted_index.damage | 6, 29, 36, 40, 52, 113, 126, 129, 134, 158, 161, 176, 189 |
| abstract_inverted_index.method | 43, 173 |
| abstract_inverted_index.sensor | 77, 85 |
| abstract_inverted_index.signal | 47, 107 |
| abstract_inverted_index.single | 125, 157 |
| abstract_inverted_index.strong | 18, 166, 193 |
| abstract_inverted_index.waves, | 96 |
| abstract_inverted_index.widely | 3 |
| abstract_inverted_index.collect | 100 |
| abstract_inverted_index.damage. | 21 |
| abstract_inverted_index.factor, | 114 |
| abstract_inverted_index.imaging | 33, 42, 54, 118, 135, 177, 191 |
| abstract_inverted_index.located | 164 |
| abstract_inverted_index.problem | 27 |
| abstract_inverted_index.realize | 123, 175 |
| abstract_inverted_index.results | 153 |
| abstract_inverted_index.rotates | 91 |
| abstract_inverted_index.sensors | 99 |
| abstract_inverted_index.signal. | 183 |
| abstract_inverted_index.through | 139 |
| abstract_inverted_index.without | 179 |
| abstract_inverted_index.Finally, | 141 |
| abstract_inverted_index.Firstly, | 65 |
| abstract_inverted_index.actuator | 89 |
| abstract_inverted_index.arranged | 80 |
| abstract_inverted_index.circular | 76 |
| abstract_inverted_index.directly | 178 |
| abstract_inverted_index.distance | 16 |
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| abstract_inverted_index.imaging. | 131 |
| abstract_inverted_index.location | 41, 53, 130, 190 |
| abstract_inverted_index.multiple | 128, 160 |
| abstract_inverted_index.original | 182 |
| abstract_inverted_index.proposed | 148 |
| abstract_inverted_index.realized | 59, 138 |
| abstract_inverted_index.signals. | 101 |
| abstract_inverted_index.spectrum | 48, 104 |
| abstract_inverted_index.verified | 72 |
| abstract_inverted_index.algorithm | 70, 119, 149 |
| abstract_inverted_index.analyzed. | 151 |
| abstract_inverted_index.calculate | 111 |
| abstract_inverted_index.clockwise | 92 |
| abstract_inverted_index.composite | 9, 56 |
| abstract_inverted_index.concerned | 4 |
| abstract_inverted_index.detection | 7 |
| abstract_inverted_index.extracted | 109 |
| abstract_inverted_index.frequency | 103 |
| abstract_inverted_index.materials | 57 |
| abstract_inverted_index.potential | 187 |
| abstract_inverted_index.proposed. | 50 |
| abstract_inverted_index.realized. | 38 |
| abstract_inverted_index.resistant | 143 |
| abstract_inverted_index.experiment | 63 |
| abstract_inverted_index.simulation | 61 |
| abstract_inverted_index.structure. | 83 |
| abstract_inverted_index.structures | 10 |
| abstract_inverted_index.extraction, | 31 |
| abstract_inverted_index.feasibility | 67 |
| abstract_inverted_index.information | 30 |
| abstract_inverted_index.positioning | 34 |
| abstract_inverted_index.probability | 117 |
| abstract_inverted_index.propagation | 15 |
| abstract_inverted_index.sensitivity | 19 |
| abstract_inverted_index.simulation. | 74 |
| abstract_inverted_index.environment. | 168, 195 |
| abstract_inverted_index.experiments. | 140 |
| abstract_inverted_index.localization | 136 |
| abstract_inverted_index.preprocessing | 180 |
| abstract_inverted_index.respectively. | 64 |
| abstract_inverted_index.characteristic | 145 |
| abstract_inverted_index.high-precision | 32 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.5799999833106995 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.67231489 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |