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Open AccessArticle

Fiber Optic Train Monitoring with Distributed Acoustic Sensing: Conventional and Neural Network Data Analysis

1
Bundesanstalt für Materialforschung und -prüfung (BAM), Unter den Eichen 87, 12205 Berlin, Germany
2
DB Netz AG, Mainzer Landstr. 199, 60326 Frankfurt, Germany
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(2), 450; https://doi.org/10.3390/s20020450 (registering DOI)
Received: 29 November 2019 / Revised: 20 December 2019 / Accepted: 24 December 2019 / Published: 13 January 2020
(This article belongs to the Special Issue Optical Fiber Sensors: Development and Applications)
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. View Full-Text
Keywords: distributed fiber optic sensing; distributed acoustic sensing; train tracking; artificial neural networks distributed fiber optic sensing; distributed acoustic sensing; train tracking; artificial neural networks
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Kowarik, S.; Hussels, M.-T.; Chruscicki, S.; Münzenberger, S.; Lämmerhirt, A.; Pohl, P.; Schubert, M. Fiber Optic Train Monitoring with Distributed Acoustic Sensing: Conventional and Neural Network Data Analysis. Sensors 2020, 20, 450.

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