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Intelligent monitoring of spatially-distributed cracks using distributed fiber optic sensors assisted by deep learning.
- Source :
-
Measurement (02632241) . Oct2023, Vol. 220, pN.PAG-N.PAG. 1p. - Publication Year :
- 2023
-
Abstract
- [Display omitted] • Cracks are monitored using distributed fiber optic sensor (DFOS) and deep learning. • A modified You Only Look Once (YOLO) model adequately interprets DFOS data. • Transfer learning is incorporated to improve the accuracy of the deep learning model. • The robustness of the proposed approach is evaluated in different test scenarios. • The mAP@0.5 of detecting spatially-distributed cracks reaches 0.968. Distributed fiber optic sensors (DFOSs) offer unique capabilities for crack monitoring via measuring strain distributions. However, manually interpreting strain distributions is labor-intensive and time-consuming. To address this challenge, this paper presents a deep learning approach for real-time automatic interpretation of strain distributions, aiming at monitoring spatially-distributed cracks. The proposed approach encompasses three key innovations. First, deep learning-based methods are developed to facilitate automatic detection and localization of spatially-distributed cracks. Second, transfer learning is incorporated to overcome the data scarcity issue in training deep learning models. This ensures robust performance even with limited data. Third, a split-and-merge method is developed, enhancing the accuracy of multi-crack detection. To evaluate the performance of the approach, experimental data from various cases were considered. The results demonstrate a mean average precision (mAP) of 0.968 for crack detection. The processing time for a set of DFOS data, containing 10,000 measurement points, was less than 0.05 s. [ABSTRACT FROM AUTHOR]
- Subjects :
- *DEEP learning
*OPTICAL fiber detectors
*STRUCTURAL health monitoring
Subjects
Details
- Language :
- English
- ISSN :
- 02632241
- Volume :
- 220
- Database :
- Academic Search Index
- Journal :
- Measurement (02632241)
- Publication Type :
- Academic Journal
- Accession number :
- 171587150
- Full Text :
- https://doi.org/10.1016/j.measurement.2023.113418