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Eingeladener Vortrag
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Maintenance and renewal costs of a typical railway, track and substructure represents 50–60% of the total costs of such infrastructure over its entire service life. Innovations in track and substructure are therefore fundamental to achieve a significant
impact on the overall cost reduction for the railways. Therefore new solutions for track improvements that are effective and that can minimize the interruption of traffic are needed. Moreover, failures of railway embankments happened recently in different regions of the world. Such events, such as the one happened in UK in February 2013 (http://www.bbc.co.uk/news/uk-england-south-yorkshire-21441070), are showing the importance of monitoring track and infrastructure coupled with the use of numerical models for the localization of the critical areas and the design of appropriate countermeasures. Indeed embankment failures, landslides and uneven settlements and similar events are becoming much more common than in the past due to climate changes, and this requires the infrastructure managers to look from a different perspective infrastructure maintenance issues. What was previously consider as “extreme” is now “common” and thus actions need to be taken to be ready when such events will happen. The aim is to mitigate their effects on the infrastructure and to minimize disruptions to train services and reduce maintenance costs to restore the normal service conditions. If this mental change happens, then the need for solutions and techniques for global asset monitoring and ground stabilization will probably increase. Among the others, geotextiles and geogrids for soil reinforcement used in combination with condition monitoring techniques have the potential for minimizing catastrophic events, whilst at the same time providing a good balance among costs and benefits (i.e. sustainability).
The paper describe a case study where the use of multifunctional geotextiles, able to provide both strengthening and monitoring functions, has been tested along a railroad near the city of Chemnitz (Germany). The results are here reported to show the potential use and the innovative aspect of this solution.
In comparison with traditional measuring Systems (pressure cells, piezometers and inclinometers which produce only point measurements) fibre optic sensors are well suited for spatiallydistributed monitoring purposes. The very tiny optical fibres of a diameter of less then 1 mm can operate as sensors in strong electromagnetic fields, under “harsh” explosive and Chemical environments and under ionizing radiation.
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.
Time-Efficient Convolutional Neural Network-Assisted Brillouin Optical Frequency Domain Analysis
(2021)
To our knowledge, this is the first report on a machine-learning-assisted Brillouin optical frequency domain analysis (BOFDA) for time-efficient temperature measurements. We propose a convolutional neural network (CNN)-based signal post-processing method that, compared to the conventional Lorentzian curve fitting approach, facilitates temperature extraction. Due to its robustness against noise, it can enhance the performance of the system. The CNN-assisted BOFDA is expected to shorten the measurement time by more than nine times and open the way for applications, where faster monitoring is essential.
Bond monitoring in temperature dependent applications using Brillouin optical time domain analyser
(2016)
Adhesive bond has to be evaluated for its integrity over a range of temperature. Adhesive is being used to bond the sensors with structures. There is no validated technique to test its performance. In this paper, we propose a Brillouin Optical Time Domain Analyzer (BOTDA) based methodology to detect temperature-induced adhesive bond failure below room temperature using distributed fiber optic sensor. The differential coefficient of thermal expansion of the structure and fiber sensor can lead to bond failure at low temperature. Optical fiber impregnated in the structure will experience differential temperature/strain due to debond of the adhesive. This leads to the frequency and amplitude decomposition of the Brillouin spectra. This is a good indication for real-time monitoring of the integrity of a bond.
A new technique for optical fiber defect detection using Brillouin distributed fiber optic sensor (DFOS) has been proposed and experimentally demonstrated in this paper. This technique is based on stimulated Brillouin scattering (SBS), which offers three wave interaction in single mode optical fiber (SMF -10 μm/125 μm acrylic coated fiber). The nonlinear effect of SBS is manipulated to locate the defect in optical fiber using distributed sensing technology. Various kind of defects may be present in optical fibers. This paper details a case study on observation of a defect, which manifests its presence in certain temperature values. The detail of defect detection through distributed fiber sensor using the SBS has been brought out. SBS is sensitive to temperature and strain. In order to study the effect of defect in distributed fiber sensor as function of temperature and strain, the distributed pre-strained and unstrained optical fiber is subjected to temperature variation and corresponding measurements are obtained with Brillouin optical time domain analyser (BOTDA). This technique enables the utilization of Brillouin parameters, such as decreased amplitude, frequency and increased linewidth in the defect region of the fiber length. The fiber defect location can be determined with spatial resolution accuracy of less than 50 cm of using BOTDA technique.
Structural health monitoring by distributed fiber optic sensors embedded into technical textiles
(2012)
Technical textiles with embedded distributed fiber optic sensors have been developed for the purposes of structural health monitoring in geotechnical and civil engineering. The distributed fiber optic sensors are based on Brillouin scattering in silica optical fibers and OTDR in polymer optical fibers. Such 'smart' technical textiles are used for reinforcement of geotechnical and masonry structures. The embedded fiber optic sensors provide online information about the condition of the structure and about the occurrence and location of any damage or degradation.--------------------------------------------------------------------------------------------------------------------------------------------------------
Technische Textilien mit integrierten faseroptischen Sensoren eröffnen neue Möglichkeiten der Zustandsüberwachung (structural health monitoring) in Geotechnik und Ingenieurbau. Die verteilt messenden Sensoren basieren auf der Brillouin-Streuung in Glasfasern und auf der OTDR in polymeroptischen Fasern. Derartige 'intelligente' technische Textilien werden in erster Line zur Verstärkung von geotechnischen Bauwerken und von Gebäuden genutzt. Die integrierten Sensoren liefern eine zeitnahe Information über den bestimmungsgemäßen Zustand des Bauwerks sowie über die Entstehung und den Ort von lokalen Bauwerksschäden.