Intelligent monitoring of spatially-distributed cracks using distributed fiber optic sensors assisted by deep learning

Yiming Liu, Yi Bao

Research output: Contribution to journalArticlepeer-review

52 Scopus citations

Abstract

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.

Original languageEnglish
Article number113418
JournalMeasurement: Journal of the International Measurement Confederation
Volume220
DOIs
StatePublished - Oct 2023

Keywords

  • Automatic data interpretation
  • Crack monitoring
  • Deep learning
  • Structural health monitoring
  • Transfer learning
  • distributed fiber optic sensor (DFOS)

Fingerprint

Dive into the research topics of 'Intelligent monitoring of spatially-distributed cracks using distributed fiber optic sensors assisted by deep learning'. Together they form a unique fingerprint.

Cite this