Abstract
We propose a novel method for the detection of vibrations caused by trains in an optical fiber buried nearby the railway track. Using optical time-domain reflectometry vibrations in the ground caused by different sources can be detected with high accuracy in time and space. While several algorithms have been proposed in the literature for train tracking using OTDR signals they have not been tested on longer recordings. The presented method learns the characteristic pattern in the Fourier domain using a support vector machine (SVM) and it becomes more robust to any kind of noise and artifacts in the signal. The point-based causal train tracking has two stages to minimize the influence of false classifications of the vibration detection. Our technical contribution is the evaluation of the presented algorithm based on two hour long recording and demonstration of open problems for commercial usage.
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Acknowledgements
The data used for the work presented in this paper have been collected in the research project Sensorsystem für Bahnstrecken funded by the Austrian Research Agency FFG under contract number 840448. We especially acknowledge Wolfgang Zottl (ÖBB Infrastruktur GmbH) for approving the measurements and Günther Neunteufel (Fiber Cable Technologies GmbH) for providing the data.
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Papp, A., Wiesmeyr, C., Litzenberger, M., Garn, H., Kropatsch, W. (2016). Train Detection and Tracking in Optical Time Domain Reflectometry (OTDR) Signals. In: Rosenhahn, B., Andres, B. (eds) Pattern Recognition. GCPR 2016. Lecture Notes in Computer Science(), vol 9796. Springer, Cham. https://doi.org/10.1007/978-3-319-45886-1_26
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