TY - JOUR
T1 - Intelligent monitoring of spatially-distributed cracks using distributed fiber optic sensors assisted by deep learning
AU - Liu, Yiming
AU - Bao, Yi
N1 - Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2023/10
Y1 - 2023/10
N2 - 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.
AB - 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.
KW - Automatic data interpretation
KW - Crack monitoring
KW - Deep learning
KW - Structural health monitoring
KW - Transfer learning
KW - distributed fiber optic sensor (DFOS)
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U2 - 10.1016/j.measurement.2023.113418
DO - 10.1016/j.measurement.2023.113418
M3 - Article
AN - SCOPUS:85167420818
SN - 0263-2241
VL - 220
JO - Measurement: Journal of the International Measurement Confederation
JF - Measurement: Journal of the International Measurement Confederation
M1 - 113418
ER -