Application of machine learning on optical fibre distributed sensing for power line applications Article Swipe
Kyriacos Kalli
,
Andreas A. Ioannou
,
Georgios Panaretou
,
Charalambos Kouzoupou
,
Maria C. Argyrou
,
Sotirios Chatzis
,
Michalis Agathocleous
,
Antreas Dionysiou
,
Efstathios Stavrakis
,
Nicolas Nicolaou
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1117/12.2666050
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1117/12.2666050
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.
Related Topics
Concepts
Computer science
Transformer
Optical fiber
Temperature measurement
Power cable
Artificial intelligence
Fault (geology)
Fault detection and isolation
Power (physics)
Real-time computing
Electronic engineering
Electrical engineering
Voltage
Engineering
Telecommunications
Materials science
Physics
Actuator
Seismology
Geology
Layer (electronics)
Quantum mechanics
Composite material
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1117/12.2666050
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4378802028
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4378802028Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1117/12.2666050Digital Object Identifier
- Title
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Application of machine learning on optical fibre distributed sensing for power line applicationsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-05-31Full publication date if available
- Authors
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Kyriacos Kalli, Andreas A. Ioannou, Georgios Panaretou, Charalambos Kouzoupou, Maria C. Argyrou, Sotirios Chatzis, Michalis Agathocleous, Antreas Dionysiou, Efstathios Stavrakis, Nicolas NicolaouList of authors in order
- Landing page
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https://doi.org/10.1117/12.2666050Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://hdl.handle.net/20.500.14279/30643Direct OA link when available
- Concepts
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Computer science, Transformer, Optical fiber, Temperature measurement, Power cable, Artificial intelligence, Fault (geology), Fault detection and isolation, Power (physics), Real-time computing, Electronic engineering, Electrical engineering, Voltage, Engineering, Telecommunications, Materials science, Physics, Actuator, Seismology, Geology, Layer (electronics), Quantum mechanics, Composite materialTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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