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Monitoring of fibre optic links with a machine learning-assisted low-cost polarimeter

Monitoring of fibre optic links with a machine learning-assisted low-cost polarimeter
Monitoring of fibre optic links with a machine learning-assisted low-cost polarimeter
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.
fibre optic sensor, Machine learning
2169-3536
183965-183971
Šlapák, Martin
08846c89-d5e9-4588-8b15-aca36c8e7faa
Vojtěch, Josef
0a0a4ae8-0146-4fcf-812d-6dc31490e781
Havliš, Ondřej
92e29d81-866b-4f13-bea6-443f87f04eb9
Slavík, Radan
2591726a-ecc0-4d1a-8e1d-4d0fd8da8f7d
Šlapák, Martin
08846c89-d5e9-4588-8b15-aca36c8e7faa
Vojtěch, Josef
0a0a4ae8-0146-4fcf-812d-6dc31490e781
Havliš, Ondřej
92e29d81-866b-4f13-bea6-443f87f04eb9
Slavík, Radan
2591726a-ecc0-4d1a-8e1d-4d0fd8da8f7d

Šlapák, Martin, Vojtěch, Josef, Havliš, Ondřej and Slavík, Radan (2020) Monitoring of fibre optic links with a machine learning-assisted low-cost polarimeter. IEEE Access, 8, 183965-183971. (doi:10.1109/ACCESS.2020.3009524).

Record type: Article

Abstract

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.

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More information

Accepted/In Press date: 7 July 2020
Published date: 15 July 2020
Keywords: fibre optic sensor, Machine learning

Identifiers

Local EPrints ID: 444874
URI: http://eprints.soton.ac.uk/id/eprint/444874
ISSN: 2169-3536
PURE UUID: d8905485-b008-4d6b-86be-b855690d94ce
ORCID for Radan Slavík: ORCID iD orcid.org/0000-0002-9336-4262

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Date deposited: 09 Nov 2020 17:30
Last modified: 28 Apr 2022 02:01

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Contributors

Author: Martin Šlapák
Author: Josef Vojtěch
Author: Ondřej Havliš
Author: Radan Slavík ORCID iD

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