TY - JOUR A1 - Kowarik, Stefan A1 - Hussels, Maria-Teresa A1 - Chruscicki, Sebastian A1 - Münzenberger, Sven A1 - Lämmerhirt, A. A1 - Pohl, P. A1 - Schubert, M. T1 - Fiber Optic Train Monitoring with Distributed Acoustic Sensing: Conventional and Neural Network Data Analysis N2 - Distributed acoustic sensing (DAS) over tens of kilometers of fiber optic cables is well-suited for monitoring extended railway infrastructures. As DAS produces large, noisy datasets, it is important to optimize algorithms for precise tracking of train position, speed, and the number of train cars, The purpose of this study is to compare different data analysis strategies and the resulting parameter uncertainties. We present data of an ICE 4 train of the Deutsche Bahn AG, which was recorded with a commercial DAS system. We localize the train signal in the data either along the temporal or spatial direction, and a similar velocity standard deviation of less than 5 km/h for a train moving at 160 km/h is found for both analysis methods, The data can be further enhanced by peak finding as well as faster and more flexible neural network algorithms. Then, individual noise peaks due to bogie clusters become visible and individual train cars can be counted. From the time between bogie signals, the velocity can also be determined with a lower standard deviation of 0.8 km/h, The analysis methods presented here will help to establish routines for near real-time Train tracking and train integrity analysis. KW - Artificial neural networks KW - Distributed fiber optic sensing KW - Distributed acoustic sensing KW - Train tracking PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-502872 DO - https://doi.org/10.3390/s20020450 VL - 20 IS - 2 SP - 450 PB - MDPI CY - Basel, Switzerland AN - OPUS4-50287 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hicke, Konstantin A1 - Chruscicki, Sebastian A1 - Münzenberger, Sven T1 - Urban traffic monitoring using Distributed Acoustic Sensing along laid fiber optic cables N2 - 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. T2 - EAGE GeoTech 2021 Second EAGE Workshop on Distributed Fibre Optic Sensing CY - Online meeting DA - 01.03.2021 KW - Distributed fiber optic sensing KW - Distributed acoustic sensing KW - DAS KW - Traffic monitoring KW - Road condition monitoring PY - 2021 DO - https://doi.org/10.3997/2214-4609.202131008 VL - 2021 SP - Paper 8, 1 PB - European Association of Geoscientists & Engineers CY - Houten, Niederlande AN - OPUS4-52214 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Liehr, Sascha A1 - Münzenberger, Sven A1 - Krebber, Katerina T1 - Wavelength-scanning coherent OTDR for dynamic high strain resolution sensing N2 - Distributed vibration sensing in optical fibers opened entirely new opportunities and penetrated various sectors from security to seismic monitoring. Here, we demonstrate a most simple and robust approach for dynamic strain measurement using wavelength-scanning coherent optical time domain reflectometry (C-OTDR). Our method is based on laser current modulation and Rayleigh backscatter shift correlation. As opposed to common single-wavelength phase demodulation techniques, also the algebraic sign of the strain change is retrieved. This is crucial for the intended applications in structural health monitoring and modal analysis. A linear strain response down to 47.5 pε and strain noise of 100 pε/√Hz is demonstrated for repetition rates in the kHz range. A field application of a vibrating bridge is presented. Our approach provides a cost-effective high-resolution method for structural vibration analysis and geophysical applications. KW - Fiber optics sensors KW - Optical time domain reflectometry KW - Rayleigh Scattering KW - Distributed acoustic sensing KW - Distributed strain sensing PY - 2018 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-448069 DO - https://doi.org/10.1364/OE.26.010573 SN - 1094-4087 VL - 26 IS - 8 SP - 10573 EP - 10588 PB - Optical Society of America AN - OPUS4-44806 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Lu, Xin A1 - Chruscicki, Sebastian A1 - Schukar, Marcus A1 - Münzenberger, Sven A1 - Krebber, Katerina T1 - Application of Intensity-Based Coherent Optical Time Domain Reflectometry to Bridge Monitoring N2 - Although distributed fiber sensing techniques have been widely used in structural health monitoring, the measurement results of bridge monitoring, particularly under destructive testing, have rarely been reported. To the best of our knowledge, this paper is the first report of distributed vibration measurement results, which we obtained during a three-day destructive test on an abolished bridge. A coherent optical time domain reflectometry (COTDR) was used to acquire the vibration information while the bridge was being sawed. The obtained signal was analyzed in time and frequency domain. Some characteristics of the sawing-induced vibration were retrieved by the short-time Fourier transform; the vibration exhibited several high frequency components within the measured range up to 20 kHz and all the components appeared in the same time slot. Some unexpected signals were also detected. Thorough analysis showed that they are quite different from the sawing-induced vibration and are believed to originate from internal damage to the bridge (probably the occurrence of cracks). KW - Destructive testing KW - Structural health monitoring KW - Distributed fiber sensing KW - Distributed acoustic sensing PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-547451 DO - https://doi.org/10.3390/s22093434 VL - 22 IS - 9 SP - 3434 PB - MDPI AN - OPUS4-54745 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Liehr, Sascha A1 - Jäger, L. A. A1 - Karapanagiotis, Christos A1 - Münzenberger, Sven A1 - Kowarik, Stefan T1 - Real-time dynamic strain sensing in optical fibers using artificial neural networks N2 - We propose to use artificial neural networks (ANNs) for raw measurement data interpolation and signal shift computation and to demonstrate advantages for wavelength-scanning coherent optical time domain reflectometry (WS-COTDR) and dynamic strain distribution measurement along optical fibers. The ANNs are trained with synthetic data to predict signal shifts from wavelength scans. Domain adaptation to measurement data is achieved, and standard correlation algorithms are outperformed. First and foremost, the ANN reduces the data analysis time by more than two orders of magnitude, making it possible for the first time to predict strain in real-time applications using the WS-COTDR approach. Further, strain noise and linearity of the sensor response are improved, resulting in more accurate measurements. ANNs also perform better for low signal-to-noise measurement data, for a reduced length of correlation input (i.e., extended distance range), and for coarser sampling settings (i.e., extended strain scanning range). The general applicability is demonstrated for distributed measurement of ground movement along a dark fiber in a telecom cable. The presented ANN-based techniques can be employed to improve the performance of a wide range of correlation or interpolation problems in fiber sensing data analysis and beyond. KW - Distributed vibration sensing KW - Distributed acoustic sensing KW - Coherent optical time domain reflectometry KW - Optical fiber sensor KW - Artificial neural network KW - Real time measurement KW - Distributed strain sensing PY - 2019 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-474832 UR - https://www.osapublishing.org/oe/abstract.cfm?uri=oe-27-5-7405 DO - https://doi.org/10.1364/OE.27.007405 SN - 1094-4087 VL - 27 IS - 5 SP - 7405 EP - 7425 PB - Optical Society of America AN - OPUS4-47483 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -