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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.
Health monitoring of ground movement via highly sensitive fiber optic sensors allows operators to detect early potential or ongoing failures in critical geotechnical structures. Particularly, the fiber optic sensors can be embedded in geosynthetics which are nowadays widely used in many geotechnical applications including earth dikes, railway embankments, landfill liners, quarries and mines. Thereby, such smart geosynthetics can be used for reinforcement, layer separation, filtration or drainage while the embedded fiber optic sensors provide information about the condition of the geotechnical structures in real time. The paper highlights the results achieved in this innovative field in the framework of several German and European projects. The presented measurement methods for long-term monitoring are based on Brillouin scattering in silica glass optical fibers (GOFs) and optical time domain reflectometry (OTDR) in polymer optical fibers (POFs).
Distributed strain and temperature change detection using optical frequency domain reflectometry
(2017)
We propose the optical frequency Domain reflectometry (OFDR) technique based on intensity modulation frequency sweep measurement for coherent distributed sensing applications. By evaluating interferometric Rayleigh scattering changes along the fibre, strain and temperature changes can be detected with a sensitivity in the ne-range and 10 mK temperature resolution. Low Vibration frequencies and vibrations up to the kHz-range can be detected by differential power change evaluation in the spatial domain. This OFDR approach is a low-cost alternative to distributed vibration sensing based on C-OTDR up to distances of several kilometres.
Distributed measurement of humidity is a sought-after capability for various fields of application, especially in the civil engineering and structural health monitoring sectors. This article presents a method for distributed humidity sensing along polymethyl methacrylate (PMMA) polymer optical fibers (POFs) by analyzing wavelength-dependent Rayleigh backscattering and attenuation characteristics at 500 nm and 650 nm wavelengths. Spatially resolved humidity sensing is obtained from backscatter traces of a dual-wavelength optical time domain reflectometer (OTDR). Backscatter dependence, attenuation dependence as well as the fiber length change are characterized as functions of relative humidity. Cross-sensitivity effects are discussed and quantified. The evaluation of the humidity-dependent backscatter effects at the two wavelength measurements allows for distributed and unambiguous measurement of relative humidity. The technique can be readily employed with low-cost standard polymer optical fibers and commercial OTDR devices.
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.
In this paper perfluorinated graded-index polymer optical fibers are characterized with respect to the influence of relative humidity changes on spectral transmission absorption and Rayleigh backscattering. The hygroscopic and thermal expansion coefficient of the fiber are determined to be CHE = (7.4 +/- 0.1) 10^-6 %r.h.^-1 and CTE = (22.7 +/- 0.3) 10^-6 K^-1, respectively. The influence of humidity on the Brillouin backscattering power and linewidth are presented for the first time to our knowledge. The Brillouin backscattering power at a pump wavelength of 1319 nm is affected by temperature and humidity. The Brillouin linewidth is observed to be a function of temperature but not of humidity. The strain coefficient of the BFS is determined to be CS = (146.5 +/- 0.9) MHz/% for a wavelength of 1319 nm within a strain range from 0.1 % to 1.5 %. The obtained results demonstrate that the humidity-induced Brillouin frequency shift is predominantly caused by the swelling of the fiber over-cladding that leads to fiber straining.
We propose to use artificial neural networks (ANNs) for raw measurement data interpolation and signal shift computation and to demonstrate advantages for wavelength-scanning coherent optical time domain reflectometry (WS-COTDR) and dynamic strain distribution measurement along optical fibers. The ANNs are trained with synthetic data to predict signal shifts from wavelength scans. Domain adaptation to measurement data is achieved, and standard correlation algorithms are outperformed. First and foremost, the ANN reduces the data analysis time by more than two orders of magnitude, making it possible for the first time to predict strain in real-time applications using the WS-COTDR approach. Further, strain noise and linearity of the sensor response are improved, resulting in more accurate measurements. ANNs also perform better for low signal-to-noise measurement data, for a reduced length of correlation input (i.e., extended distance range), and for coarser sampling settings (i.e., extended strain scanning range). The general applicability is demonstrated for distributed measurement of ground movement along a dark fiber in a telecom cable. The presented ANN-based techniques can be employed to improve the performance of a wide range of correlation or interpolation problems in fiber sensing data analysis and beyond.