Filtern
Erscheinungsjahr
- 2022 (5) (entfernen)
Dokumenttyp
- Beitrag zu einem Tagungsband (2)
- Vortrag (2)
- Zeitschriftenartikel (1)
Schlagworte
- I-OFDR (2)
- BOFDA (1)
- Backscatter measurement (1)
- Brillouin distributed fiber sensor (1)
- Brillouin distributed sensing (1)
- Distributed POF sensor (1)
- Distributed sensing (1)
- Fiber optics sensors (1)
- Humidity sensing (1)
- Machine learning (1)
Organisationseinheit der BAM
Eingeladener Vortrag
- nein (2)
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.
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.
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.
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.