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The optical fibres widely used in telecommunication can be simultaneously used for (distributed) sensing or fibre network self-monitoring. In our work, we monitor changes in the fibre environment via monitoring changes in the state of light polarization without the utilization of methods based on back-scattered light. These changes can generate a vast amount of data, but it is generally not straightforward to extract useful information from them, e.g., future fibre break predictions or earthquake monitoring. We suggest using machine learning to solve this problem. However, since the measured data events are not labelled (i.e., we do not know in advance what fingerprint in the measured data corresponds to a future fibre break), unsupervised machine learning methods must be used. Here, we report a proof-of-concept approach in which we use a simple polarimetric technique and installed optical fibre, which we disturb with controlled vibrations, knocking on the fibre, and rack door closing near the fibre. Using a machine learning K-means algorithm, we distinguish between data generated with these controlled disturbances and data generated by noise due to common traffic. These results are the first step along the way to automated data labelling, which can be used for the classification of events.

Monitoring of Fibre Optic Links With a Machine Learning-Assisted Low-Cost Polarimeter