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Eingeladener Vortrag
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Wir präsentieren unsere Forschungsergebnisse zur Entwicklung eines faseroptischen Sensorsystems mit polymeroptischen Fasern integriert in Geokunststoffe für die Überwachung von geotechnischen Anlagen. Das Sensorsystem dient der räumlichen Auflösung lokaler Dehnungsereignisse im für Anwender signifikanten Größenordnungsbereich von 3 % bis 10 %. Das gesamte Sensorkonzept besteht aus einem praxistauglichen Messsystem basierend auf der digitalen inkohärenten optischen Frequenzbereichsreflektometrie (I-OFDR) und einer Sensormatte in Form eines Geotextils mit integrierten faseroptischen perfluorierten polymeroptischen Fasern (PF-POFs).
We present a prototype of a distributed POF sensing system for strain detection in road construction and civil engineering ready to be used in practical applications. The system is based on a cost-efficient digital incoherent optical frequency domain reflectometry (I-OFDR). In this approach, the strain-induced changes in the Rayleigh backscattering profile along a perfluorinated polymer optical fiber (PF-POF) are determined by measuring the complex transfer function of the sensing fiber using a compact digital data acquisition unit. The digital unit replaces an oversized vector network analyzer (VNA) enabling suitability of the measurement system for out-door use and providing a significant reduction of the total system costs at the same time. The entire sensor con-cept includes the use of geosynthetics with integrated PF-POFs as two-dimensional sensor structures for geotech-nical applications. The robustness and the functionality of the distributed POF I-OFDR sensors were proven in the field. The field test involved embedding of two geomats, each with two polymer optical sensor loops, into the embankment of the federal road B 91 in a section near Leipzig. The measurements conducted after the sensor-based geomats had been installed confirmed both the potential of such smart geosynthetics and the functionali-ty of the digital I-OFDR prototype.
We present a high-precision fiber-optic temperature sensor based on a polymer possessing a Lower Critical Solution Temperature (LCST) in aqueous solution encapsulated in a capillary. For a temperature higher than the LCST the transparent homogenous polymer solution changes into a suspension which forms an opaque fluid. The resulting temperature-dependent turbidity influences the transmission characteristics measured using a simple fiber-optic system.
The presented development provides a small-sized, robust temperature sensor, patented by the BAM (patent nr.: EP 3 043 161 B1), easy to integrate in many application fields, especially for process monitoring.
We report on the development of a field-proven distributed fiber optic sensing system for structural health monitoring in road construction and civil engineering. The system is based on a cost-efficient digital incoherent optical frequency domain reflectometry (I-OFDR) for distributed strain detection along a polymer optical fiber. In this method, the strain-induced backscatter increase in a graded-index multimode perfluorinated polymer optical fiber (PF-POF) is determined by measuring the complex transfer function of the sensing fiber using a compact digital data acquisition unit.
In this paper, a cost-efficient distributed fiber optic measurement system based on Rayleigh scattering is presented. The distributed digital incoherent optical frequency domain reflectometry (I-OFDR) method is developed for detection of significantly large strain in the range from 3 % up to 10 % as required by end users. For this purpose, a vector network analyzer used in the I-OFDR is replaced by a compact and cost-effective digital data acquisition system. This digital emitting/receiving unit enables the recording of the complex transfer function carrying information about the local deformations along the entire sensing fiber.
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.
Es wird ein faseroptisches Messsystem vorgestellt, welches auf Basis der Rayleigh-Rückstreumessung eine ortsauflösende Detektion von signifikant großen Dehnungen bei Bauvorhaben im von Anwendern geforderten Größenordnungsbereich von 3 % bis 10 % ermöglicht. Mit dem Verfahren der digitalen inkohärenten optischen Frequenzbereichsreflektometrie (I-OFDR) werden materialspezifische Vorteile robuster perfluorierter polymeroptischer Fasern (PF-POF) erschlossen. Für den angestrebten industriellen Einsatz für die Zustandsüberwachung im Bereich des Tunnel- und Spezialtiefbaus soll die spezifische Rückstreuzunahme in der PF-POF hochauflösend gemessen werden. Diese Veröffentlichung zeigt in ersten Projektergebnissen die Eignung des digitalen I-OFDR für eine Dehnungsdetektion von bis zu 10%.
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.
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.