Low-Rank Representation and Data Compression of Full-Field Displacement Maps for Structural Modal Analysis and Damage Identification
Stereo-digital image correlation (DIC) is promising in structural vibration testing due to its advantages of non-contact, full-field, and high-spatial resolution. However, thousands of full-field displacement maps generated by stereo-DIC hamper its practical applications. Furthermore, with the evalu...
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MDPI AG
2025-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/11/3449 |
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| author | Yankun Li Yu Huang Ziguang Li Zhiping Yin Shancheng Cao |
| author_facet | Yankun Li Yu Huang Ziguang Li Zhiping Yin Shancheng Cao |
| author_sort | Yankun Li |
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| description | Stereo-digital image correlation (DIC) is promising in structural vibration testing due to its advantages of non-contact, full-field, and high-spatial resolution. However, thousands of full-field displacement maps generated by stereo-DIC hamper its practical applications. Furthermore, with the evaluated mode shapes, how to accurately reveal the embedded/hidden damage positions without a reference dataset is another critical problem. For the purpose of resolving those issues, a complete method is proposed, consisting of (1) an adaptive kernel function construction method for low-rank-representing the full-field displacement maps while retaining both global and local shape features, (2) an enhanced frequency domain decomposition approach for noise-robust mode shape estimation based on the kernel functions, and (3) extracting and fusing local shape features of multiple mode shapes for more accurate damage localization. Finally, numerical and experimental case studies are investigated to verify the effectiveness and accuracy of the proposed low-rank representation method in modal parameters and damage identification. In addition, it is found that the mode shape and damage characteristics of displacement fields can be captured by the first 20 principal components, and the first six modes provide robust damage localization results. |
| format | Article |
| id | doaj-art-0d9d1ca2d14642d2a2f6e3ef72af7ed5 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-0d9d1ca2d14642d2a2f6e3ef72af7ed52025-08-20T03:46:45ZengMDPI AGSensors1424-82202025-05-012511344910.3390/s25113449Low-Rank Representation and Data Compression of Full-Field Displacement Maps for Structural Modal Analysis and Damage IdentificationYankun Li0Yu Huang1Ziguang Li2Zhiping Yin3Shancheng Cao4School of Astronautics, Northwestern Polytechnical University, Xi’an 710072, ChinaBeijing Institute of Astronautical Systems Engineering, Beijing 100076, ChinaBeijing Institute of Astronautical Systems Engineering, Beijing 100076, ChinaSchool of Civil Aviation, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Astronautics, Northwestern Polytechnical University, Xi’an 710072, ChinaStereo-digital image correlation (DIC) is promising in structural vibration testing due to its advantages of non-contact, full-field, and high-spatial resolution. However, thousands of full-field displacement maps generated by stereo-DIC hamper its practical applications. Furthermore, with the evaluated mode shapes, how to accurately reveal the embedded/hidden damage positions without a reference dataset is another critical problem. For the purpose of resolving those issues, a complete method is proposed, consisting of (1) an adaptive kernel function construction method for low-rank-representing the full-field displacement maps while retaining both global and local shape features, (2) an enhanced frequency domain decomposition approach for noise-robust mode shape estimation based on the kernel functions, and (3) extracting and fusing local shape features of multiple mode shapes for more accurate damage localization. Finally, numerical and experimental case studies are investigated to verify the effectiveness and accuracy of the proposed low-rank representation method in modal parameters and damage identification. In addition, it is found that the mode shape and damage characteristics of displacement fields can be captured by the first 20 principal components, and the first six modes provide robust damage localization results.https://www.mdpi.com/1424-8220/25/11/3449full-field displacement mapsdigital image correlationoperational modal analysisdamage identificationlow-rank representation |
| spellingShingle | Yankun Li Yu Huang Ziguang Li Zhiping Yin Shancheng Cao Low-Rank Representation and Data Compression of Full-Field Displacement Maps for Structural Modal Analysis and Damage Identification Sensors full-field displacement maps digital image correlation operational modal analysis damage identification low-rank representation |
| title | Low-Rank Representation and Data Compression of Full-Field Displacement Maps for Structural Modal Analysis and Damage Identification |
| title_full | Low-Rank Representation and Data Compression of Full-Field Displacement Maps for Structural Modal Analysis and Damage Identification |
| title_fullStr | Low-Rank Representation and Data Compression of Full-Field Displacement Maps for Structural Modal Analysis and Damage Identification |
| title_full_unstemmed | Low-Rank Representation and Data Compression of Full-Field Displacement Maps for Structural Modal Analysis and Damage Identification |
| title_short | Low-Rank Representation and Data Compression of Full-Field Displacement Maps for Structural Modal Analysis and Damage Identification |
| title_sort | low rank representation and data compression of full field displacement maps for structural modal analysis and damage identification |
| topic | full-field displacement maps digital image correlation operational modal analysis damage identification low-rank representation |
| url | https://www.mdpi.com/1424-8220/25/11/3449 |
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