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Monitoring of construction projects plays increasingly important role in the implementation of growing needs and requirements, especially in geologically difficult areas. This particularly concerns tunnelling and special civil engineering projects. The use of fiber optic sensors for structural health monitoring provides significant economic advantages regarding lower life-cycle costs of the civil infrastructure. We present our achievements in the development of distributed polymer optical fiber sensors based on Rayleigh and Brillouin scattering for early damage detection.
We present our achievements in the development of distributed fiber optic sensing systems based on Brillouin optical frequency-domain analysis for structural health monitoring. The focus of the applications is on the gapless monitoring of geotechnical structures, large area infrastructures and electrical grids. The work includes the latest use of machine learning algorithms to reduce measurement time by coexistent increasing the measurement accuracy.
We compare four different sensing solutions suitable for distributed fiber optic humidity sensing in per uorinated graded-index polymer optical fibers (PFGI-POFs). Compared to silica fbers, polymer optical fibers over advantageous beneffits including signifficantly higher break down strain, fracture toughness and humidity sensitivity. Various humidity-related effects in PFGI-POFs have been reported in the last years including measured attenuation and length changes as well as Brillouin frequency and Bragg wavelength shifts. The four aforementioned methods could serve as a basis for distributed and quasi-distributed humidity sensing and are described here closely with an emphasis on plausible cross effects to temperature and strain. The main focus of this paper lies on the comparison of four approaches with regard to method complexity, sensitivity to humidity, spatial resolution, real-time capability and effort to compensate for cross effects.
We present our research on the development of a Brillouin optical frequency-domain analysis (BOFDA) using a perfluorinated graded-index polymer optical fiber (PFGI-POF) as a sensing fiber. The described works include investigations both on the selection of the setup components with respect to mode coupling effects and on the impact of the humidity cross-sensitivity. The suitability of the developed POF-based BOFDA is proved by a distributed measurement on an 86 m long PFGI-POF recorded with the spatial resolution of 6 m.
We present our research on the development of a Brillouin optical frequency-domain analysis (BOFDA) using a perfluorinated graded-index polymer optical fiber (PFGI-POF) as a sensing fiber. The described works include investigations both on the selection of the setup components with respect to mode coupling effects and on the impact of the humidity cross-sensitivity. The suitability of the developed POF-based BOFDA is proved by a distributed measurement on an 86 m long PFGI-POF recorded with the spatial resolution of 6 m.
The substantial progresses in fiber-optic communications in combination with the increasing economic and political interest in structural health monitoring have led to a commercial establishment of distributed Brillouin sensing. The sensor systems, mainly based on the time-domain techniques, have been successfully implemented in the areas such as pipeline leak detection, geohazard effects, and ground movement detection.
This chapter introduces a further advancement in the area of Brillouin sensing in the frequency domain. The so-called Brillouin optical frequency-domain analysis (BOFDA) offers crucial perspectives in terms of dynamic range and cost efficiency.
The main principle of the frequency-domain approach takes advantage of the reversibility between the time and frequency domain given by a Fourier transform in the analysis of linear systems. The hereby presented overview gives a summary of the benefits and challenges of frequency-domain measurements closely tied to the narrowband recording of the complex transfer function. This function relates the counterpropagating pump and probe laser light along the sensor fiber providing the pulse response of the measurement system by applying the inverse Fourier transform (IFT). The strain or temperature distribution can be then determined from the retrieved Brillouin frequency shift (BFS) profile along the fiber.
In this work, we present our results achieved in several research activities for development of distributed fiber optic radiation sensors using glass and polymer optical fibers. The findings show that both the measurement of the radiation-induced attenuation (RIA) along the entire sensing fiber and the accompanying change in the refractive index of the fiber core can be used for distributed radiation monitoring.
In this work, we present our results achieved in several research activities for development of fiber optic dosimeters. The findings show that both the measurement of the radiation-induced attenuation (RIA) along the entire sensing fiber and the accom panying change in the refractive index of the fiber core can be used for distributed radiation monitoring in the kGy and MGy range, respectively. Depending on the fiber type and material the RIA shows varying response to dose rates, environmental temperatures and the wavelength of the laser source used.
Distributed humidity fiber-optic sensor based on BOFDA using a simple machine learning approach
(2022)
We report, to our knowledge for the first time, on distributed relative humidity sensing in silica polyimide-coated optical fibers using Brillouin optical frequency domain analysis (BOFDA). Linear regression, which is a simple and well-interpretable algorithm in machine learning and statistics, is utilized. The algorithm is trained using as features the Brillouin frequency shifts and linewidths of the fiber’s multipeak Brillouin spectrum. To assess and improve the effectiveness of the regression algorithm, we make use of machine learning concepts to estimate the model’s uncertainties and select the features that contribute most to the model’s performance. In addition to relative humidity, the model is also able to simultaneously provide distributed temperature information addressing the well-known cross-sensitivity effects.