An international Railway Infrastructure company wants to monitor train traffic levels by using optical fiber sensors. This ensures safe and cost-effective train operations in the transportation industry. Optical fiber sensors can provide distributed sensing (spatially continuous) over large distances. Therefore, with optical fiber over a rail its whole length can be continuously monitored.
The company contact Terminus7 to develop an advance signal processing solution through Machine Learning, looking for monitoring train position and train traffic. This company also contact Terminus7 to develop an infrastructure monitoring solution (see Infrastructure Monitoring use case)
Optical Fiber signal processing to monitor trains
We used Terminus7 Signal Processing to solve train tracking in two steps.
First, we normalise noisy signal over the installation. The optical fiber demonstrates not to be regular through the rail and so it signals under the same situation. Therefore, we applied Machine Learning advanced techniques to find a reference pattern over which normalise every signal.
In the second step, we applied Machine Learning to detect signal patterns related to the train presence in different rail paths. The detection also take into account train models that potentially produce different optical fiber signal patterns.
This is a WIP project. Our results demonstrate the ability of Terminus7 to find different train models patterns and to determine its position, speed and acceleration with high accuracy. Current work is focused on model fine tuning to speed the solution to its best accuracy.
Machine Learning signal processing for train detection
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