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Intelligent detecti...
Intelligent detection of warning bells at level crossings through deep transfer learning for smarter railway maintenance
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- De Donato, Lorenzo (författare)
- University of Naples Federico II, Italy,Univ Naples Federico II, Italy
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- Marrone, Stefano (författare)
- University of Naples Federico II, Italy,Univ Naples Federico II, Italy
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- Flammini, Francesco, Senior Lecturer, 1978- (författare)
- Linnéuniversitetet,Mälardalens universitet,Innovation och produktrealisering,Department of Computer Science and Media Technology, Linnaeus University, Växjö, 351 95, Sweden,Institutionen för datavetenskap och medieteknik (DM),Mälardalen University, Sweden
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- Sansone, Carlo (författare)
- University of Naples Federico II, Italy,Univ Naples Federico II, Italy
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- Vittorini, Valeria (författare)
- University of Naples Federico II, Italy,Univ Naples Federico II, Italy
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- Nardone, Roberto (författare)
- University of Naples “Parthenope”, Italy,Univ Naples Parthenope, Italy
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- Mazzariello, Claudio (författare)
- Digital & Lumada Solutions, Hitachi Rail STS, Italy
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- Bernaudin, Frederic (författare)
- Digital & Lumada Solutions, Hitachi Rail STS, Italy
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(creator_code:org_t)
- Elsevier Ltd, 2023
- 2023
- Engelska.
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Ingår i: Engineering applications of artificial intelligence. - : Elsevier Ltd. - 0952-1976 .- 1873-6769. ; 123
- Relaterad länk:
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https://doi.org/10.1...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- Level Crossings are among the most critical railway assets, concerning both the risk of accidents and their maintainability, due to intersections with promiscuous traffic and difficulties in remotely monitoring their health status. Failures can be originated from several factors, including malfunctions in the bar mechanisms and warning devices, such as light signals and bells. This paper focuses on the intelligent detection of anomalies in warning bells through non-intrusive acoustic monitoring by: (1) introducing a new concept for autonomous monitoring of level crossings; (2) generating and sharing a specific dataset collecting relevant audio signals from publicly available audio recordings; (3) implementing and evaluating a solution combining deep learning and transfer learning for warning bell detection. The results show a high accuracy in detecting anomalies and suggest viability of the approach in real-world applications, especially where network cameras with on-board microphones are installed for multi-purpose level crossing surveillance.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Annan samhällsbyggnadsteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Other Civil Engineering (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Systemvetenskap, informationssystem och informatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Information Systems (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Transportteknik och logistik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Transport Systems and Logistics (hsv//eng)
Nyckelord
- Anomaly detection
- Artificial intelligence
- Audio analytics
- Machine learning
- Predictive maintenance
- Railway safety
- Bells
- Deep learning
- Health risks
- Learning systems
- Railroad accidents
- Railroad crossings
- Railroad transportation
- Audio analytic
- Intelligent detection
- Level crossing
- Machine-learning
- Railway maintenance
- Risk of accidents
- Transfer learning
- Railroads
- Informatik
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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