Vision-Based Damage Detection Method Using Multi-Scale Local Information Entropy and Data Fusion
Low-spatial-resolution measurements from contact sensors and excessive measurement noise have impeded the implementation of vibration-based damage detection. To tackle these challenges, we propose a novel vision-based damage detection method combining multi-scale signal analysis theory and data fusi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/803 |
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| author | Yiming Zhang Zili Xu Guang Li Cun Xin |
| author_facet | Yiming Zhang Zili Xu Guang Li Cun Xin |
| author_sort | Yiming Zhang |
| collection | DOAJ |
| description | Low-spatial-resolution measurements from contact sensors and excessive measurement noise have impeded the implementation of vibration-based damage detection. To tackle these challenges, we propose a novel vision-based damage detection method combining multi-scale signal analysis theory and data fusion algorithm. For high-spatial-resolution vibration measurements, phase-based optical flow estimation algorithm is adopted to deploy virtual sensors on the structure, yielding reliable mode shapes. We then introduce the concept of entropy into damage detection. A novel damage index, defined in Gaussian multi-scale space and named multi-scale local information entropy (MS-LIE), is proposed. The MS-LIE integrates the multi-scale analysis component and the entropy analysis component, addressing both the issue of detection sensitivity and noise immunity, thereby showcasing enhanced performance. Moreover, a data fusion technique for multi-scale damage information is developed to further mitigate the noise-induced uncertainty and pinpoint damage locations. A series of numerical and experimental scenarios are designed to validate the method, and the results indicate that the proposed method accurately detects single and multiple damages in noisy environments, obviating the need for baseline data as a reference. |
| format | Article |
| id | doaj-art-7982e7c1ff38412583bc007ddc225981 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-7982e7c1ff38412583bc007ddc2259812025-01-24T13:20:52ZengMDPI AGApplied Sciences2076-34172025-01-0115280310.3390/app15020803Vision-Based Damage Detection Method Using Multi-Scale Local Information Entropy and Data FusionYiming Zhang0Zili Xu1Guang Li2Cun Xin3State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi’an Jiaotong University, Xi’an 710049, ChinaState Key Laboratory for Strength and Vibration of Mechanical Structures, Xi’an Jiaotong University, Xi’an 710049, ChinaState Key Laboratory for Strength and Vibration of Mechanical Structures, Xi’an Jiaotong University, Xi’an 710049, ChinaState Key Laboratory for Strength and Vibration of Mechanical Structures, Xi’an Jiaotong University, Xi’an 710049, ChinaLow-spatial-resolution measurements from contact sensors and excessive measurement noise have impeded the implementation of vibration-based damage detection. To tackle these challenges, we propose a novel vision-based damage detection method combining multi-scale signal analysis theory and data fusion algorithm. For high-spatial-resolution vibration measurements, phase-based optical flow estimation algorithm is adopted to deploy virtual sensors on the structure, yielding reliable mode shapes. We then introduce the concept of entropy into damage detection. A novel damage index, defined in Gaussian multi-scale space and named multi-scale local information entropy (MS-LIE), is proposed. The MS-LIE integrates the multi-scale analysis component and the entropy analysis component, addressing both the issue of detection sensitivity and noise immunity, thereby showcasing enhanced performance. Moreover, a data fusion technique for multi-scale damage information is developed to further mitigate the noise-induced uncertainty and pinpoint damage locations. A series of numerical and experimental scenarios are designed to validate the method, and the results indicate that the proposed method accurately detects single and multiple damages in noisy environments, obviating the need for baseline data as a reference.https://www.mdpi.com/2076-3417/15/2/803damage detectionoptical flowlocal information entropymulti-scale analysisdata fusionnoisy environments |
| spellingShingle | Yiming Zhang Zili Xu Guang Li Cun Xin Vision-Based Damage Detection Method Using Multi-Scale Local Information Entropy and Data Fusion Applied Sciences damage detection optical flow local information entropy multi-scale analysis data fusion noisy environments |
| title | Vision-Based Damage Detection Method Using Multi-Scale Local Information Entropy and Data Fusion |
| title_full | Vision-Based Damage Detection Method Using Multi-Scale Local Information Entropy and Data Fusion |
| title_fullStr | Vision-Based Damage Detection Method Using Multi-Scale Local Information Entropy and Data Fusion |
| title_full_unstemmed | Vision-Based Damage Detection Method Using Multi-Scale Local Information Entropy and Data Fusion |
| title_short | Vision-Based Damage Detection Method Using Multi-Scale Local Information Entropy and Data Fusion |
| title_sort | vision based damage detection method using multi scale local information entropy and data fusion |
| topic | damage detection optical flow local information entropy multi-scale analysis data fusion noisy environments |
| url | https://www.mdpi.com/2076-3417/15/2/803 |
| work_keys_str_mv | AT yimingzhang visionbaseddamagedetectionmethodusingmultiscalelocalinformationentropyanddatafusion AT zilixu visionbaseddamagedetectionmethodusingmultiscalelocalinformationentropyanddatafusion AT guangli visionbaseddamagedetectionmethodusingmultiscalelocalinformationentropyanddatafusion AT cunxin visionbaseddamagedetectionmethodusingmultiscalelocalinformationentropyanddatafusion |