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A study on the appl...
A study on the application of convolutional neural networks for the maintenance of railway tracks
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- Pappaterra, M. J. (författare)
- Department of Computer Science, Uppsala University, Uppsala, Sweden
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- Pappaterra, M. L. (författare)
- Facultad de Matemática, Astronomía, Física y Computación (FaMAF), Universidad Nacional de Córdoba (UNC), Córdoba, Argentina
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- Flammini, Francesco, Senior Lecturer, 1978- (författare)
- Mälardalens universitet,Innovation och produktrealisering,IDSIA USI-SUPSI, University of Applied Sciences and Arts of Southern Switzerland, Lugano, Switzerland
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(creator_code:org_t)
- Springer Nature, 2024
- 2024
- Engelska.
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Ingår i: Discover Artificial Intelligence. - : Springer Nature. - 2731-0809. ; 4:1
- Relaterad länk:
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https://doi.org/10.1...
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visa fler...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- This paper provides an overview of the applications of Convolutional Neural Networks (CNN) in the railway maintenance industry. Our research covers specifically the subdomain of railway track maintenance. In this study, we have analyzed the state-of-the-art of CNNs applied to railway track maintenance by conducting an extensive literature review, summarizing different tasks and problems related to the topic and presenting solutions based on CNNs with a special emphasis on the data used to create these models. The results of our research show different applications of CNNs within the scope, including the detection of defects in the surface of railway rails and railway track components, such as fasteners, joints, sleepers, switches and crossings, as well as the recognition of track components, and the continuous monitoring of railway tracks. The architecture of CNNs is fitting to learning spatial hierarchies of features directly from the input data, making them of great use for Computer Vision and other applications related to the topic at hand. The implementation of IoT devices and smart sensors aid the collection of real-time data which can be used to feed powerful CNN models to recognize patterns and identify complex events related to the maintenance of railway tracks. This and more insights are discussed in detail within the contents of this paper.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Maskinteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Mechanical Engineering (hsv//eng)
Nyckelord
- Artificial Intelligence
- Convolutional Neural Networks
- Datasets
- Literature review
- Maintenance
- Railways
- Convolution
- Railroad tracks
- Railroads
- Convolutional neural network
- Dataset
- Literature reviews
- Railway
- Railway maintenance
- Railway track
- State of the art
- Subdomain
- Track components
- Track maintenance
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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