Robust Damage Detection and Localization Under Complex Environmental Conditions Using Singular Value Decomposition-based Feature Extraction and One-dimensional Convolutional Neural Network
Abstract Ultrasonic guided wave is an attractive monitoring technique for large-scale structures but is vulnerable to changes in environmental and operational conditions (EOC), which are inevitable in the normal inspection of civil and mechanical structures. This paper thus presents a robust guided...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-05-01
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| Series: | Chinese Journal of Mechanical Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s10033-023-00889-3 |
| _version_ | 1797827617306443776 |
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| author | Shengkang Zong Sheng Wang Zhitao Luo Xinkai Wu Hui Zhang Zhonghua Ni |
| author_facet | Shengkang Zong Sheng Wang Zhitao Luo Xinkai Wu Hui Zhang Zhonghua Ni |
| author_sort | Shengkang Zong |
| collection | DOAJ |
| description | Abstract Ultrasonic guided wave is an attractive monitoring technique for large-scale structures but is vulnerable to changes in environmental and operational conditions (EOC), which are inevitable in the normal inspection of civil and mechanical structures. This paper thus presents a robust guided wave-based method for damage detection and localization under complex environmental conditions by singular value decomposition-based feature extraction and one-dimensional convolutional neural network (1D-CNN). After singular value decomposition-based feature extraction processing, a temporal robust damage index (TRDI) is extracted, and the effect of EOCs is well removed. Hence, even for the signals with a very large temperature-varying range and low signal-to-noise ratios (SNRs), the final damage detection and localization accuracy retain perfect 100%. Verifications are conducted on two different experimental datasets. The first dataset consists of guided wave signals collected from a thin aluminum plate with artificial noises, and the second is a publicly available experimental dataset of guided wave signals acquired on a composite plate with a temperature ranging from 20°C to 60°C. It is demonstrated that the proposed method can detect and localize the damage accurately and rapidly, showing great potential for application in complex and unknown EOC. |
| first_indexed | 2024-04-09T12:51:12Z |
| format | Article |
| id | doaj.art-052fc67965454044892a53a7a73013ba |
| institution | Directory Open Access Journal |
| issn | 2192-8258 |
| language | English |
| last_indexed | 2024-04-09T12:51:12Z |
| publishDate | 2023-05-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Chinese Journal of Mechanical Engineering |
| spelling | doaj.art-052fc67965454044892a53a7a73013ba2023-05-14T11:10:40ZengSpringerOpenChinese Journal of Mechanical Engineering2192-82582023-05-0136111010.1186/s10033-023-00889-3Robust Damage Detection and Localization Under Complex Environmental Conditions Using Singular Value Decomposition-based Feature Extraction and One-dimensional Convolutional Neural NetworkShengkang Zong0Sheng Wang1Zhitao Luo2Xinkai Wu3Hui Zhang4Zhonghua Ni5Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, School of Mechanical Engineering, Southeast UniversityJiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, School of Mechanical Engineering, Southeast UniversityJiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, School of Mechanical Engineering, Southeast UniversityJiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, School of Mechanical Engineering, Southeast UniversityJiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, School of Mechanical Engineering, Southeast UniversityJiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, School of Mechanical Engineering, Southeast UniversityAbstract Ultrasonic guided wave is an attractive monitoring technique for large-scale structures but is vulnerable to changes in environmental and operational conditions (EOC), which are inevitable in the normal inspection of civil and mechanical structures. This paper thus presents a robust guided wave-based method for damage detection and localization under complex environmental conditions by singular value decomposition-based feature extraction and one-dimensional convolutional neural network (1D-CNN). After singular value decomposition-based feature extraction processing, a temporal robust damage index (TRDI) is extracted, and the effect of EOCs is well removed. Hence, even for the signals with a very large temperature-varying range and low signal-to-noise ratios (SNRs), the final damage detection and localization accuracy retain perfect 100%. Verifications are conducted on two different experimental datasets. The first dataset consists of guided wave signals collected from a thin aluminum plate with artificial noises, and the second is a publicly available experimental dataset of guided wave signals acquired on a composite plate with a temperature ranging from 20°C to 60°C. It is demonstrated that the proposed method can detect and localize the damage accurately and rapidly, showing great potential for application in complex and unknown EOC.https://doi.org/10.1186/s10033-023-00889-3Ultrasonic guided wavesSingular value decompositionDamage detection and localizationEnvironmental and operational conditionsOne-dimensional convolutional neural network |
| spellingShingle | Shengkang Zong Sheng Wang Zhitao Luo Xinkai Wu Hui Zhang Zhonghua Ni Robust Damage Detection and Localization Under Complex Environmental Conditions Using Singular Value Decomposition-based Feature Extraction and One-dimensional Convolutional Neural Network Chinese Journal of Mechanical Engineering Ultrasonic guided waves Singular value decomposition Damage detection and localization Environmental and operational conditions One-dimensional convolutional neural network |
| title | Robust Damage Detection and Localization Under Complex Environmental Conditions Using Singular Value Decomposition-based Feature Extraction and One-dimensional Convolutional Neural Network |
| title_full | Robust Damage Detection and Localization Under Complex Environmental Conditions Using Singular Value Decomposition-based Feature Extraction and One-dimensional Convolutional Neural Network |
| title_fullStr | Robust Damage Detection and Localization Under Complex Environmental Conditions Using Singular Value Decomposition-based Feature Extraction and One-dimensional Convolutional Neural Network |
| title_full_unstemmed | Robust Damage Detection and Localization Under Complex Environmental Conditions Using Singular Value Decomposition-based Feature Extraction and One-dimensional Convolutional Neural Network |
| title_short | Robust Damage Detection and Localization Under Complex Environmental Conditions Using Singular Value Decomposition-based Feature Extraction and One-dimensional Convolutional Neural Network |
| title_sort | robust damage detection and localization under complex environmental conditions using singular value decomposition based feature extraction and one dimensional convolutional neural network |
| topic | Ultrasonic guided waves Singular value decomposition Damage detection and localization Environmental and operational conditions One-dimensional convolutional neural network |
| url | https://doi.org/10.1186/s10033-023-00889-3 |
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