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Wavelength-scanning distributed acoustic sensing for structural monitoring and seismic applications
(2019)
We introduce wavelength-scanning coherent optical time domain reflectometry (WS-COTDR) for dynamic vibration sensing along optical fibers. The method is based on spectral shift computation from Rayleigh backscatter spectra. Artificial neural networks (ANNs) are used for fast and high-resolution strain computation from raw measurement data. The applicability of the method is demonstrated for vibration monitoring of a reinforced concrete bridge. We demonstrate another application example for quasi-static and dynamic measurement of ground deformation and surface wave propagation along a dark fiber in a telecommunication cable.
Distributed vibration sensing in optical fibers opened entirely new opportunities and penetrated various sectors from security to seismic monitoring. Here, we demonstrate a most simple and robust approach for dynamic strain measurement using wavelength-scanning coherent optical time domain reflectometry (C-OTDR). Our method is based on laser current modulation and Rayleigh backscatter shift correlation. As opposed to common single-wavelength phase demodulation techniques, also the algebraic sign of the strain change is retrieved. This is crucial for the intended applications in structural health monitoring and modal analysis. A linear strain response down to 47.5 pε and strain noise of 100 pε/√Hz is demonstrated for repetition rates in the kHz range. A field application of a vibrating bridge is presented. Our approach provides a cost-effective high-resolution method for structural vibration analysis and geophysical applications.
Time-Efficient Convolutional Neural Network-Assisted Brillouin Optical Frequency Domain Analysis
(2021)
To our knowledge, this is the first report on a machine-learning-assisted Brillouin optical frequency domain analysis (BOFDA) for time-efficient temperature measurements. We propose a convolutional neural network (CNN)-based signal post-processing method that, compared to the conventional Lorentzian curve fitting approach, facilitates temperature extraction. Due to its robustness against noise, it can enhance the performance of the system. The CNN-assisted BOFDA is expected to shorten the measurement time by more than nine times and open the way for applications, where faster monitoring is essential.
In the last few years, the use of machine learning has emerged in the field of distributed fiber optic sensors as a promising approach to enhance their performance and provide new capabilities. In this study, we use machine learning for simultaneous measurements of temperature and humidity in polyimide (PI)-coated optical fibers based on Brillouin Brillouin optical frequency domain analysis (BOFDA). Different non-linear machine learning algorithms are employed, namely polynomial regression, decision trees and artificial neural networks (ANNs), and their discrimination performance is benchmarked against that of the conventional linear regression. The performance is evaluated using leave-one-out cross-validation to ensure that the models are reliable and able to generalize well on new data. We show that nonlinear machine learning algorithms outperform the conventional linear regression and thus could pave the way towards simultaneous cost-effective temperature and humidity distributed sensing, which has the potential to find attractive new applications in the field of civil and geotechnical engineering, from structural health monitoring of dikes and bridges to subsea cables and long pipelines corrosion detection.
Solution doping of commercial polymethyl methacrylate (PMMA) polymer optical fibers (POFs) is presented as a novel approach for preparation of custom cladding-doped POFs (CD-POFs). The presented method is based on a solution-mediated diffusion of dopant molecules into the fiber cladding upon soaking of POFs in a methanol-dopant solution. The method was tested on three different commercial POFs using Rhodamine B as a fluorescent dopant. The dynamics of the diffusion process was studied in order to optimize the doping procedure in terms of selection of the most suitable POF, doping time and conditions. Using the optimized procedure, longer segment of fluorescent CD-POF was prepared and its performance was characterized. Fiber’s potential for sensing and illumination applications was demonstrated and discussed. The proposed method represents a simple and cheap way for fabrication of custom, short to medium length CD-POFs with various dopants.
Solution doping of off-the-shelf plastic optical fibers (POFs) represents rather simple and cheap way for preparing custom cladding-doped POFs (CD-POFs) with short to medium lengths. CD-POFs are especially attractive for environmental sensing applications, but might be of interest for illumination task as well. In this work, the proposed doping technique is tested with three different commercial low-cost polymethyl methacrylate (PMMA) POFs; Eska CK-40 and Eska GK-40 from Mitsubishi Rayon and Raytela PGU-FB1000 from Toray. The aim of the work is to aid the selection of the most suitable fiber yielding highest optical quality of prepared CD-POFs. Firstly, the optimal doping times are determined for the individual fiber types using short fiber samples. Secondly, longer 10 m CD-POFs are prepared from all tested fibersusing the optimized doping procedure. Finally, attenuation of pristine POFs and prepared CD-POFs is measured using optical time domain reflectometry in order to characterize the impacts of the doping on fiber optical properties. In addition, the importance of post-doping drying procedure for CD-POF optical performance is investigated as well. The results suggest that, although doping of all tested fibers is generally feasible, Eska CK-40 is the most suitable candidate with regard to the Doping efficiency and fiber post-doping performance.
Smart geosynthetics with embedded optical fibers as distributed sensors provide solutions both for applications in geotechnical engineering and for cost-effective monitoring of critical infrastructures. The incorporation of glass or polymer optical fibers (GOFs or POFs) in geotextiles and geogrids allows early detection of mechanical deformations, temperature and humidity. This paper presents selected examples of smart geosynthetics based on Brillouin and Rayleigh scattering effects in incorporated fiber optic sensors for monitoring of large geotechnical structures like dikes, dams, railways, embankments or slopes. The focus of the presented work is on real field tests of measurement capability with respect to the chosen measurement principle and used fiber type.
We present a technique for distributed temperature gradient sensing in real-time along an optical fiber utilizing simple amplitude-based direct-detection coherent optical time domain reflectometry (C-OTDR) and a special sensing fiber. Our technique enables us to determine phase changes or low-frequency variations of the C-OTDR signal stemming from temperature variations. The distinct feature of the used sensing fiber is its structuring with equidistant strongly scattering dots. Consecutive pairs of these scatterers form the dominant local interferometers, effectively overwriting the otherwise highly nonlinear transfer function of common optical fiber. This enables a quasi-phase-resolved evaluation of perturbation responses originating from temperature changes at sensor positions between the scatterers. Using our method, we show the measurement of a nonlinear temperature transient from a heating process with a maximum temperature gradient of 0.8 °C over 20 s and a total temperature increase of 28.4 °C. This method requires almost no post-processing and can be used for simultaneous distributed vibration sensing (DVS) and quantification of local temperature gradients in a single fiber, e.g., for the use in condition monitoring of infrastructure or industrial installations.
Geometric phase measured per beat period in a ϕ-OTDR based on coherent heterodyne detection is used to measure strain. Proposed method is robust to polarisation mismatch fading as a polarisation mismatch between interfering beams is not a hindrance to the measurement of the geometric phase. The Geometric phase is a function of the intensities of the interfering beams as well as the envelope of the beat signal. Its calculation does not require phase unwrapping and accordingly does not suffer the phase unwrapping errors. It is required to be equated with the traditionally measured phase by applying a scaling factor. The spatial resolution of the measured strain is reduced as it is calculated per beat period. Results are verified using a piezo-electric transducer inline a fiber-under-test.