| Title |
Multi-fidelity Data-fusion for Improving Strain accuracy using Optical Fiber Sensors |
| Authors |
박영수(Park, Young-Soo) ; 진승섭(Jin, Seung-Seop) ; 유철환(Yoo, Chul-Hwan) ; 김성태(Kim, Sungtae) ; 박영환(Park, Young-Hwan) |
| DOI |
https://doi.org/10.12652/Ksce.2020.40.6.0547 |
| Keywords |
다정밀도 모델링;광섬유 센서;변형률;가우시안 프로세스 회귀;상보적 데이터 융합 Multi-fidelity modeling;Optical fiber sensor;Strain;Gaussian process regression;Complementary data-fusion |
| Abstract |
As aging infrastructures increase along with time, the efficient maintenance becomes more significant and accurate responses from the sensors are pre-requisite. Among various responses, strain is commonly used to detect damage such as crack and fatigue. Optical fiber sensor is one of the promising sensing techniques to measure strains with high-durability, immunity for electrical noise, long transmission distance. Fiber Bragg Grating (FBG) is a point sensor to measure the strain based on reflected signals from the grating, while Brillouin Optic Correlation Domain Analysis (BOCDA) is a distributed sensor to measure the strain along with the optical fiber based on scattering signals. Although the FBG provides the signal with high accuracy and reproducibility, the number of sensing points is limited. On the other hand, the BOCDA can measure a quasi-continuous strain along with the optical fiber. However, the measured signals from BOCDA have low accuracy and reproducibility. This paper proposed a multi-fidelity data-fusion method based on Gaussian Process Regression to improve the fidelity of the strain distribution by fusing the advantages of both systems. The proposed method was evaluated by laboratory test. The result shows that the proposed method is promising to improve the fidelity of the strain. |