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Faseroptische Sensorsysteme bieten heute die Möglichkeit des Online-Monitorings von sensiblen, für die Energieversorgung wichtigen Betriebsmitteln. Insbesondere Hochenergiekabel, wie diese sowohl im Offshore- als auch im Onshorebereich eingesetzt werden, sind prädestiniert für eine zustandsorientierte Überwachung. Gerade Hochspannungshochenergiekabel sind Betriebsmittel, die im Falle eines Ausfalls durch Schäden mit hohen Reparatur- und Folgekosten verbunden sind. Mit dem Einsatz von faseroptischen Sensoren sind mechanische Einflüsse wie Vibrationen z. B. verursacht durch Ankerfall, Temperaturhotspots oder auch Teilentladungsaktivitäten an dezidierten vulnerablen Stellen wie Kabelendverschlüssen oder –muffen über längere Kabelstrecken detektierbar. Der Einsatz von im Rahmen des Verbundprojekts Monalisa entwickelter faseroptischer Diagnosetechnik in Verbindung mit faseroptischen Sensoren wird hier aufgezeigt.
We present a technique for distributed temperature gradient sensing in real-time along an optical fiber utilizing simple amplitude-based direct-detection coherent optical time domain reflectometry (C-OTDR) and a special sensing fiber. Our technique enables us to determine phase changes or low-frequency variations of the C-OTDR signal stemming from temperature variations. The distinct feature of the used sensing fiber is its structuring with equidistant strongly scattering dots. Consecutive pairs of these scatterers form the dominant local interferometers, effectively overwriting the otherwise highly nonlinear transfer function of common optical fiber. This enables a quasi-phase-resolved evaluation of perturbation responses originating from temperature changes at sensor positions between the scatterers. Using our method, we show the measurement of a nonlinear temperature transient from a heating process with a maximum temperature gradient of 0.8 °C over 20 s and a total temperature increase of 28.4 °C. This method requires almost no post-processing and can be used for simultaneous distributed vibration sensing (DVS) and quantification of local temperature gradients in a single fiber, e.g., for the use in condition monitoring of infrastructure or industrial installations.
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.
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.
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.
This contribution introduces an investigation of a large-scale prestressed concrete bridge model (“BLEIB” structure at the BAM-TTS open air test site) by means of on-site cooperative measurements. This bridge has an external post-tensioning system and has been instrumented with the ultrasonic transducers, temperature sensors and optical fibers for Distributed Acoustic Sensing (DAS). Our experiment was designed to test the suitability of the novel 6C sensors developed within the framework of the GIOTTO project – the IMU50. The IMU50 sensor enables vibration measurements in translation along three axes and rotation around three axes. The geophone sensors were considered for complementary measurements of vertical velocity response. In the experiment, several perturbations were achieved by controlling the external influence factors such as loading and prestressing changes. The aim of the integrated measurement strategy was to fully observe the results of the condition change and to verify the effectiveness of multiple sensors for bridge monitoring.
Wavelength scanning coherent optical time domain reflectometer (WS-COTDR) is a good candidate to spatially resolve the environmental information at comparatively low frequency. This paper reveals it can also work as a traditional optical time domain reflectometer (OTDR) to identify Fresnel reflection by averaging the obtained signal over the wavelength scanning range. Simultaneous distributed vibration sensing and a traditional OTDR measurement are experimentally demonstrated using the WS-COTDR system.