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Verteilte faseroptische Sensorik eignet sich sehr gut für eine Zustands- und Gefährdungsüberwachung von ausgedehnten Hochspannungskabelinstallationen. Basierend auf der Messung von verschiedenen faseroptischen Rückstreueffekten in der Sensorfaser lassen sich ortsaufgelöste Profile von Messgrößen wie Temperatur, Dehnung oder Vibrationen erzielen.
Hier demonstrieren wir die Leistungsfähigkeit verschiedener faseroptischer Sensorikansätze bezüglich dieser Größen anhand von Experimenten u.a. an einem Mittelspannungskabel. Des Weiteren zeigen wir, wie die Verwendung von verschiedenen Sensorkabeltypen mit unterschiedlicher Applikation auf der Kabeloberfläche die Messergebnisse bei einem Biegeversuch beeinflussen und schlussfolgern damit auf die für die Einbettung in Kabeln geeigneten Sensoren.
After describing the basics of distributed fiber optic sensing in general and distributed fiber optic acoustic sensing (DAS) in particular, several currently and previously investigated fields of application of DASin infrastructure monitoring and seismic monitoring are presented. A focus is placed on seismic measurements and DAS measurements determining the dynamic responses of concrete structures to certain stimuli.
The feasibility of traffic monitoring along a major urban road using Distributed Acoustic Sensing (DAS) is demonstrated. We present measurement results of conventional intensity-based DAS along a dark fiber in a tubed fiber optic cable buried roadside. With only minimal postprocessing different classes of traffic participants can be distinguished (cars, bikes, pedestrians). Different methods for pre-processing raw data are compared, especially with regard to providing suitable inputs for pattern recognition algorithms. Furthermore, the various challenges for automatized vehicle detection and classification related to varying sensitivity and inhomogeneous signal propagation are discussed. Sensitivity fluctuations and variations are in part inherent to the measurement technology and originate in part in ground conditions. The measurement quality and usefulness for traffic monitoring of intensity-based DAS is compared to that of state-of-the-art phase-resolved DAS, allowing quantitative evaluation of vibration signals.
The feasibility of traffic monitoring along a major urban road using Distributed Acoustic Sensing (DAS) is demonstrated. We present measurement results of conventional intensity-based DAS along a dark fiber in a tubed fiber optic cable buried roadside. With only minimal postprocessing different classes of traffic participants can be distinguished (cars, bikes, pedestrians). Different methods for pre-processing raw data are compared, especially with regard to providing suitable inputs for pattern recognition algorithms. Furthermore, the various challenges for automatized vehicle detection and classification related to varying sensitivity and inhomogeneous signal propagation are discussed. Sensitivity fluctuations and variations are in part inherent to the measurement technology and originate in part in ground conditions. The measurement quality and usefulness for traffic monitoring of intensity-based DAS is compared to that of state-of-the-art phase-resolved DAS, allowing quantitative evaluation of vibration signals.
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.
In the last few years, the use of machine learning has emerged in the field of distributed fiber optic sensors as a promising approach to enhance their performance and provide new capabilities. In this study, we use machine learning for simultaneous measurements of temperature and humidity in polyimide (PI)-coated optical fibers based on Brillouin Brillouin optical frequency domain analysis (BOFDA). Different non-linear machine learning algorithms are employed, namely polynomial regression, decision trees and artificial neural networks (ANNs), and their discrimination performance is benchmarked against that of the conventional linear regression. The performance is evaluated using leave-one-out cross-validation to ensure that the models are reliable and able to generalize well on new data. We show that nonlinear machine learning algorithms outperform the conventional linear regression and thus could pave the way towards simultaneous cost-effective temperature and humidity distributed sensing, which has the potential to find attractive new applications in the field of civil and geotechnical engineering, from structural health monitoring of dikes and bridges to subsea cables and long pipelines corrosion detection.
Darstellung des Verbundvorhabens "Monalisa" und der im Rahmen des Projekts erzielten F&E-Fortschritte auf dem 2. Statusseminar "Zukunftsfähige Stromnetze" des PTJ zur gleichnamigen Forschungsinitative der Bundesregierung. Präsentiert werden Fortschungsergebnisse zum Thema Zustandsüberwachung von verschiedenen elektrischen Betriebsmitteln (Kabel, Garnituren, Schaltanlagen, Schalter) mittels faseroptischer Sensorik. Das Monitoring bezieht sich dabei auf Temperatur, Vibrationen, eindringende Feuchte, Dehnung, und die Detektion von Teilentladungen
Selbstdarstellung des Verbundvorhabens "Monalisa" auf der Konferenz "VDE Hochspannungstechnik" 2018 der entsprechenden Forschungsinitiative der Bundesregierung Kurzzusammenfassung der Teilprojekte und F&E-Ziele aller beteiligten Partnerinstitutionen (BAM,TU Berlin, HTW Berlin, BeuthHS Berlin, IPH GmbH) zum Bereich Monitoring von elektrischen Betriebsmitteln mit Hilfe von faseroptischer Sensorik.