Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/30643
Title: Application of machine learning on optical fibre distributed sensing for power line applications
Authors: Kalli, Kyriacos 
Ioannou, Andreas 
Panaretou, Georgios 
Kouzoupou, Charalambos 
Argyrou, Maria C. 
Chatzis, Sotirios P. 
Editors: Lieberman, Robert A. 
Baldini, Francesco 
Homola, Jiri 
Major Field of Science: Engineering and Technology
Field Category: Electrical Engineering - Electronic Engineering - Information Engineering
Keywords: distributed sensing;machine learning;Optical fibres
Issue Date: 24-Apr-2023
Source: Optical Sensors 2023, Prague, Czech Republic, 24 - 26 April 2023
Journal: Proceedings of SPIE - The International Society for Optical Engineering 
Abstract: We present a study on the application of machine learning to optical fibre distributed sensing, with data recovered using a state-of-the-art, commercial BOTDR distributed sensing system; temperature information was extracted from the power line distribution networks that are part of the Electricity Authority of Cyprus. A machine learning approach was implemented for the prediction task of finding points of abnormal behaviour, mimicking the power cable joints that are prone to failure, along with general monitoring for unusual behaviour and potential cable fault conditions; the task is a binary classification one. Labels “0/1” were assigned to the BOTDR measurements, with “1” corresponding to data points in space and time for which the signal showcased a problematic scenario, such as that recorded by optical fibres that are collocated with power cables where the fibre’s temperature measurement increases to dangerously high values, and conversely “0” for all other scenarios. The algorithm’s base is a variation of the state-of-the-art transformer architecture, which depends solely on attention mechanisms. The field data recovered show the potential of the algorithm to predict spatiotemporally problematic points, using the temperature measurements of the collocated fibre.
URI: https://hdl.handle.net/20.500.14279/30643
ISBN: 9781510662643
ISSN: 0277786X
DOI: 10.1117/12.2666050
Rights: © SPIE
Attribution-NonCommercial-NoDerivatives 4.0 International
Type: Conference Papers
Affiliation : Cyprus University of Technology 
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation

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