Filtern
Dokumenttyp
Sprache
- Englisch (5)
- Mehrsprachig (1)
Schlagworte
- Brillouin sensing (6) (entfernen)
Organisationseinheit der BAM
Eingeladener Vortrag
- nein (1)
We report on the development of a complete system for spatially resolved detection of critical soil displacement in river embankments. The system uses Brillouin frequency domain analysis (BOFDA) for distributed measurement of strain in silica optical fibers. Our development consists of the measurement unit, an adequate coating for the optical fibers and a technique to integrate the coated optical fibers into geotextiles as they are commonly used in dike construction. We present several laboratory and field tests that prove the capability of the system to detect areas of soil displacement as small as 2 meters. These are the first tests of truly distributed strain measurements on optical fibers embedded into geosynthetics.
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.
This paper reports on the application of distributed Brillouin fiber optic sensing to the monitoring of geotechnical structures. The presented approach to Brillouin sensing offers an alternative to the common pulse-based measurements by using the frequency domain analysis.
The focus of the research and field-testing work is to collect reliable structural health monitoring data from large-scale structures while preserving the System’s versatility in terms of being suitable for different application environments (soil structures, foundations, concrete construction works, pipelines etc.) and its robustness in being well functioning in harsh environments. Upon reviewing the presented application test, it is concluded that Brillouin frequency domain based monitoring, when applied to geotechnical structures, is capable of providing accurate strain readings representing deformations of the soil body, and reliable temperature readings for generating temperature profiles of the entire structure.
An overview of the frequency domain analysis of Brillouin sensing and its digital implementation is given, along with the experiences and insights drawn from an open field test of sensor-equipped geogrid mats.
In this paper, we present a collection of machine learning assisted distributed fiber optic sensors (DFOS) for applications in the field of infrastructure monitoring. We employ advanced signal processing based on artificial neural networks (ANNs) to enhance the performance of the dynamic DFOS for strain and vibration sensing. Specifically, ANNs in comparison to conventional and computationally expensive correlation and linearization algorithms, deliver lower strain errors and speed up the signal processing allowing real time strain monitoring. Furthermore, convolutional neural networks (CNNs) are used to denoise the dynamic DFOS signal and enable useable sensing lengths of up to 100 km. Applications of the machine learning assisted dynamic DFOS in road traffic and railway infrastructure monitoring are demonstrated. In the field of static DFOS, machine learning is applied to the well-known Brillouin optical frequency domain analysis (BOFDA) system. Specifically, CNN are shown to be very tolerant against noisy spectra and contribute towards significantly shorter measurement times. Furthermore, different machine learning algorithms (linear and polynomial regression, decision trees, ANNs) are applied to solve the well-known problem of cross-sensitivity in cases when temperature and humidity are measured simultaneously. The presented machine learning assisted DFOS can potentially contribute towards enhanced, cost effective and reliable monitoring of infrastructures.