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 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-502872 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 - Kowarik, Stefan A1 - Hicke, Konstantin A1 - Chruscicki, Sebastian A1 - Schukar, Marcus A1 - Breithaupt, Mathias A1 - Lämmerhirt, A. A1 - Pohl, P. A1 - Schubert, M. ED - Cranch, G. ED - Wang, A. ED - Digonnet, M. ED - Dragic, P. T1 - Train monitoring using distributed fiber optic acoustic sensing N2 - We use distributed acoustic sensing to determine the velocity of trains from train vibration patterns using artificial neural network and conventional algorithms. The velocity uncertainty depends on track conditions, train type and velocity. T2 - 27th International Conference on Optical Fiber Sensors (OFS) CY - Meeting was canceled DA - 08.06.2020 KW - Train monitoring KW - Distributed acoustic sensing KW - DAS KW - Distributed fiber optic sensing PY - 2020 SN - 978-1-55752-307-5 U6 - https://doi.org/10.1364/OFS.2020.T3.25 SP - 1 EP - 4 PB - The Optical Society (Optical Society of America) CY - Washington D.C., USA AN - OPUS4-50758 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 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-474832 UR - https://www.osapublishing.org/oe/abstract.cfm?uri=oe-27-5-7405 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 -