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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.
We report, to our knowledge, for the first time on humidity-induced Brillouin frequency shifts in perfluorinated graded index polymer optical fibers. A linear relation between Brillouin frequency shift and humidity was observed. Furthermore, the humidity coefficient of the Brillouin frequency shift is demonstrated to be a function of temperature (-107 to -64 kHz/%r.h. or -426 to -49 kHz m3/g in the range of 20 to 60 °C). An analytical description proves temperature and humidity as two mutually independent effects on the Brillouin frequency shift.
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.
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.
A long distance range over tens of kilometers is a prerequisite for a wide range of distributed fiber optic vibration sensing applications. We significantly extend the attenuation-limited distance range by making use of the multidimensionality of distributed Rayleigh backscatter data: Using the wavelength-scanning coherent optical time domain reflectometry (WS-COTDR) technique, backscatter data is measured along the distance and optical frequency dimensions. In this work, we develop, train, and test deep convolutional neural networks (CNNs) for fast denoising of these two-dimensional backscattering results. The very compact and efficient CNN denoiser “DnOTDR” outperforms state-of-the-art image denoising algorithms for this task and enables denoising data rates of 1.2 GB/s in real time. We demonstrate that, using the CNN denoiser, the quantitative strain measurement with nm/m resolution can be conducted with up to 100 km distance without the use of backscatter-enhanced fibers or distributed Raman or Brillouin amplification.
X-ray reflectivity (XRR) is a powerful and popular scattering technique that can give valuable insight into the growth behavior of thin films. This study Shows how a simple artificial neural network model can be used to determine the thickness, roughness and density of thin films of different organic semiconductors [diindenoperylene, copper(II) phthalocyanine and alpha-sexithiophene] on silica from their XRR data with millisecond computation time and with minimal user input or a priori knowledge. For a large experimental data set of 372 XRR curves, it is shown that a simple fully connected model can provide good results with a mean absolute percentage error of 8–18% when compared with the results obtained by a genetic least mean squares fit using the classical Parratt formalism. Furthermore, current drawbacks and prospects for improvement are discussed.
The distributed measurement of relative humidity is a sought-after capability for a wide range of applications in civil engineering and structural health monitoring. We show that polymethyl methacrylate (PMMA) optical fi-bers can be employed as a sensor medium to conduct distributed humidity measurement by analyzing Rayleigh backscattering traces obtained by OTDR. We make use of the effect that water penetrates the fiber core and directly influences the local fiber attenuation and Rayleigh backscatter coefficient. We conducted distributed backscattering analysis for two different pulse wavelengths: 500 nm and 650 nm. The 650 nm results are susceptible to both, attenuation changes and backscatter changes, whereas backscatter results at 500 nm are not affected by humidity-induced attenuation and only exhibit a change of Rayleigh backscattered power as a function of humidity. The combined measurement and analysis of both parameters at these two wavelengths has the advantage that cross-sensitivities on backscatter change and attenuation, such as strain and tempera-ture changes, could be separated from the humidity response of the fiber. We present laboratory results for a humidity range between 30% and 90% for both pulse wavelengths: including step responses, humidity cycles and hysteresis analysis. In addition to the attenuation and backscatter coefficient dependence, we also analyze optical runtime changes as a function of humidity. POFs have the advantage that they can be directly embed-ded into materials such as concrete or soil to measure water content or localize water ingress. Standard step-index PMMA POFs can be used as a distributed relative humidity sensor up to 200 m distance.
The use of artificial neural networks (ANNs) is demonstrated for efficient real-time data processing in optical fiber sensing applications. Using ANN-based algorithms, two orders of magnitude improved computation time and improved measurement resolution is achieved for distributed strain sensing using the wavelength-scanning coherent optical time domain reflectometry technique.
This chapter gives an overview about polymer optical fiber (POF) sensors with the focus on structural and civil engineering applications. POF properties such as the high-strain range, the low Young’s modulus, and specific scattering effects open new fields for fiber-optic sensing applications. POF properties, sensitivities, and cross-sensitivities that are relevant for sensing are introduced. Advantages and limitations are discussed. State-of-the-art POF sensors and application examples are presented in subsections with regard to their underlying measurement principles.