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 - 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 -