Quasi-Distributed Fiber Bragg Grating Sensing Using Stepped Incoherent Optical Frequency Domain Reflectometry

Werzinger, Stefan; Bergdolt, Sven; Engelbrecht, Rainer; Thiel, Torsten; Schmauß, Bernhard

doi:10.1109/jlt.2016.2614581

Cited by 39 publications

(25 citation statements)

References 23 publications

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“…In our system, the Bragg wavelength of a FBG sensor is determined by examining the reflectivity during a stepped wavelength scan of a tunable laser source (TLS; Werzinger et al, 2016). An array of several FBGs with the same or similar Bragg wavelengths can be separated spatially by performing IOFDR measurements at each wavelength step.…”

Section: Fiber Bragg Grating Temperature Sensingmentioning

confidence: 99%

Combined distributed Raman and Bragg fiber temperature sensing using incoherent optical frequency domain reflectometry

Koeppel

Werzinger

Ringel

et al. 2018

J. Sens. Sens. Syst.

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Optical temperature sensors offer unique features which make them indispensable for key industries such as the energy sector. However, commercially available systems are usually designed to perform either distributed or distinct hot spot temperature measurements since they are restricted to one measurement principle. We have combined two concepts, fiber Bragg grating (FBG) temperature sensors and Raman-based distributed temperature sensing (DTS), to overcome these limitations. Using a technique called incoherent optical frequency domain reflectometry (IOFDR), it is possible to cascade several FBGs with the same Bragg wavelength in one fiber and simultaneously perform truly distributed Raman temperature measurements. In our lab we have achieved a standard deviation of 2.5 K or better at a spatial resolution in the order of 1 m with the Raman DTS. We have also carried out a field test in a high-voltage environment with strong magnetic fields where we performed simultaneous Raman and FBG temperature measurements using a single sensor fiber only.Published by Copernicus Publications on behalf of the AMA Association for Sensor Technology.

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Section: Fiber Bragg Grating Temperature Sensingmentioning

confidence: 99%

Combined distributed Raman and Bragg fiber temperature sensing using incoherent optical frequency domain reflectometry

Koeppel

Werzinger

Ringel

et al. 2018

J. Sens. Sens. Syst.

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“…The curvature sensitivity and spatial resolution for distributed shape sensing have been investigated [12]. In order to overcome the limitation of curvature measurement of single-core grating, in recent years, multi-core fiber has been used to measure shape, and the research of curvature sensing has attracted more and more attention [13][14][15][16][17][18]. In 2000, Flockhart used multi-core gratings to measure bending deformation, and the curvature error was 0.14 m -1 [19].…”

Section: Introductionmentioning

confidence: 99%

2D and 3D Shape Sensing Based on 7-Core Fiber Bragg Gratings

et al. 2020

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A fiber-optic shape sensing based on 7-core fiber Bragg gratings (FBGs) is proposed and experimentally demonstrated. The investigations are presented for two-dimensional and three-dimensional shape reconstruction by distinguishing bending and twisting of 7-core optical fiber with FBGs. The curvature and bending orientation can be calculated by acquiring FBG wavelengths from any two side cores among the six outer cores. And the shape sensing in three-dimensional (3D) space is computed by analytic geometry theory. The experiments corresponding of two-dimensional (2D) and 3D shape sensing are demonstrated and conducted to verify the theoretical principles. The resolution of curvature is about 0.1m−1 for 2D measuring. The error of angle in shape reconstruction is about 1.89° for 3D measuring. The proposed sensing technique based on 7-core FBGs is promising of high feasibility, stability, and repeatability, especially for the distinguishing ability on the bending orientation due to the six symmetrical cores on the cross-section.

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“…In particular, reflectometer-based distributed OFS provide a huge amount of sensing points over many kilometers of range [ 1 , 2 , 3 , 4 ]. Conventional signal processing methods for such distributed OFS include optical pulse coding [ 5 , 6 ], Fourier transform [ 7 , 8 , 9 ] or wavelet transform processing [ 10 , 11 ]. In recent years, however, more and more advanced digital signal processing concepts, for example based on image processing [ 12 ] and machine learning [ 13 ], have been applied to further enhance the performance of distributed OFS.…”

Section: Introductionmentioning

confidence: 99%

“…Model-based signal processing [ 14 , 15 ] can be a very efficient technique in this context, as system states and sensor information can be estimated, using specifically tailored parametric models, also exploiting prior knowledge of the sensors. In this paper, we propose and demonstrate a novel model-based processing concept for quasi-distributed sensing with fiber Bragg grating (FBG) arrays, providing increased efficiency compared to conventional techniques, in particular Fourier transform processing [ 7 ]. The developed models provide great flexibility and could be easily expanded to meet different system configurations or could be used in connection with machine learning algorithms, for example.…”

Section: Introductionmentioning

confidence: 99%

Model-Based Position and Reflectivity Estimation of Fiber Bragg Grating Sensor Arrays

Werzinger

Zibar

Köppel

et al. 2018

Sensors

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We propose an efficient model-based signal processing approach for optical fiber sensing with fiber Bragg grating (FBG) arrays. A position estimation based on an estimation of distribution algorithm (EDA) and a reflectivity estimation method using a parametric transfer matrix model (TMM) are outlined in detail. The estimation algorithms are evaluated with Monte Carlo simulations and measurement data from an incoherent optical frequency domain reflectometer (iOFDR). The model-based approach outperforms conventional Fourier transform processing, especially near the spatial resolution limit, saving electrical bandwidth and measurement time. The models provide great flexibility and can be easily expanded in complexity to meet different topologies and to include prior knowledge of the sensors. Systematic errors due to crosstalk between gratings caused by multiple reflections and spectral shadowing could be further considered with the TMM to improve the performance of large-scale FBG array sensor systems.

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Quasi-Distributed Fiber Bragg Grating Sensing Using Stepped Incoherent Optical Frequency Domain Reflectometry

Cited by 39 publications

References 23 publications

Combined distributed Raman and Bragg fiber temperature sensing using incoherent optical frequency domain reflectometry

Combined distributed Raman and Bragg fiber temperature sensing using incoherent optical frequency domain reflectometry

2D and 3D Shape Sensing Based on 7-Core Fiber Bragg Gratings

Model-Based Position and Reflectivity Estimation of Fiber Bragg Grating Sensor Arrays

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