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Calibrating static measurement data from distributed fiber optics by the integration of limited FBG sensors based on the extended kernel regression method

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Abstract
Thanks to the rapid development of fiber optic sensors, the arrival of distributed sensors makes continuous and dense measurement possible for real structures. The distributed fiber optic sensors could provide a very high number of sensors along one single fiber, which is of great significance for structural health monitoring, especially for the detection of the damage position. However, the accuracy of data measured from fiber optic distributed sensors, such as strain and displacement etc., are sometimes not as accurate as those obtained from traditional fiber optic sensors such as fiber Bragg gratings (FBGs) and strain gauges. In this paper, to enhance the accuracy of static strain data measured from distributed fiber optic sensors, an extended kernel regression (EKR) method is applied to combine the distributed sensor measurement with those from four FBG sensors. These provide quite accurate static strain data; the strain values at locations where the FBGs are absent can therefore be predicted by the EKR method, which uses the data from distributed fiber optics as a biased model. The static experimental activities have been carried out in a laboratory, using a cantilever beam structure under different static loads.
Keywords
Instrumentation, Applied Mathematics, FREQUENCY-DOMAIN REFLECTOMETRY, PERFORMANCE, TECHNOLOGY, SCATTERING, LIGHT, data calibration, FBGs, distributed fiber optics, EKR model

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MLA
Cheng, Liangliang, et al. “Calibrating Static Measurement Data from Distributed Fiber Optics by the Integration of Limited FBG Sensors Based on the Extended Kernel Regression Method.” MEASUREMENT SCIENCE AND TECHNOLOGY, vol. 30, no. 12, 2019, doi:10.1088/1361-6501/ab3530.
APA
Cheng, L., Cigada, A., Lang, Z.-Q., & Zappa, E. (2019). Calibrating static measurement data from distributed fiber optics by the integration of limited FBG sensors based on the extended kernel regression method. MEASUREMENT SCIENCE AND TECHNOLOGY, 30(12). https://doi.org/10.1088/1361-6501/ab3530
Chicago author-date
Cheng, Liangliang, Alfredo Cigada, Zi-Qiang Lang, and Emanuele Zappa. 2019. “Calibrating Static Measurement Data from Distributed Fiber Optics by the Integration of Limited FBG Sensors Based on the Extended Kernel Regression Method.” MEASUREMENT SCIENCE AND TECHNOLOGY 30 (12). https://doi.org/10.1088/1361-6501/ab3530.
Chicago author-date (all authors)
Cheng, Liangliang, Alfredo Cigada, Zi-Qiang Lang, and Emanuele Zappa. 2019. “Calibrating Static Measurement Data from Distributed Fiber Optics by the Integration of Limited FBG Sensors Based on the Extended Kernel Regression Method.” MEASUREMENT SCIENCE AND TECHNOLOGY 30 (12). doi:10.1088/1361-6501/ab3530.
Vancouver
1.
Cheng L, Cigada A, Lang Z-Q, Zappa E. Calibrating static measurement data from distributed fiber optics by the integration of limited FBG sensors based on the extended kernel regression method. MEASUREMENT SCIENCE AND TECHNOLOGY. 2019;30(12).
IEEE
[1]
L. Cheng, A. Cigada, Z.-Q. Lang, and E. Zappa, “Calibrating static measurement data from distributed fiber optics by the integration of limited FBG sensors based on the extended kernel regression method,” MEASUREMENT SCIENCE AND TECHNOLOGY, vol. 30, no. 12, 2019.
@article{8664924,
  abstract     = {{Thanks to the rapid development of fiber optic sensors, the arrival of distributed sensors makes continuous and dense measurement possible for real structures. The distributed fiber optic sensors could provide a very high number of sensors along one single fiber, which is of great significance for structural health monitoring, especially for the detection of the damage position. However, the accuracy of data measured from fiber optic distributed sensors, such as strain and displacement etc., are sometimes not as accurate as those obtained from traditional fiber optic sensors such as fiber Bragg gratings (FBGs) and strain gauges. In this paper, to enhance the accuracy of static strain data measured from distributed fiber optic sensors, an extended kernel regression (EKR) method is applied to combine the distributed sensor measurement with those from four FBG sensors. These provide quite accurate static strain data; the strain values at locations where the FBGs are absent can therefore be predicted by the EKR method, which uses the data from distributed fiber optics as a biased model. The static experimental activities have been carried out in a laboratory, using a cantilever beam structure under different static loads.}},
  articleno    = {{125102}},
  author       = {{Cheng, Liangliang and Cigada, Alfredo and Lang, Zi-Qiang and Zappa, Emanuele}},
  issn         = {{0957-0233}},
  journal      = {{MEASUREMENT SCIENCE AND TECHNOLOGY}},
  keywords     = {{Instrumentation,Applied Mathematics,FREQUENCY-DOMAIN REFLECTOMETRY,PERFORMANCE,TECHNOLOGY,SCATTERING,LIGHT,data calibration,FBGs,distributed fiber optics,EKR model}},
  language     = {{eng}},
  number       = {{12}},
  pages        = {{14}},
  title        = {{Calibrating static measurement data from distributed fiber optics by the integration of limited FBG sensors based on the extended kernel regression method}},
  url          = {{http://dx.doi.org/10.1088/1361-6501/ab3530}},
  volume       = {{30}},
  year         = {{2019}},
}

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