De Donato, Lorenzo (2023) Deep Learning for railway safety and maintenance: methodologies and applications. [Tesi di dottorato]
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| Item Type: | Tesi di dottorato |
|---|---|
| Resource language: | English |
| Title: | Deep Learning for railway safety and maintenance: methodologies and applications |
| Creators: | Creators Email De Donato, Lorenzo lorenzo.dedonato@unina.it |
| Date: | 13 December 2023 |
| Number of Pages: | 236 |
| Institution: | Università degli Studi di Napoli Federico II |
| Department: | Ingegneria Elettrica e delle Tecnologie dell'Informazione |
| Dottorato: | Information technology and electrical engineering |
| Ciclo di dottorato: | 36 |
| Coordinatore del Corso di dottorato: | nome email Russo, Stefano stefano.russo@unina.it |
| Tutor: | nome email Vittorini, Valeria UNSPECIFIED Sansone, Carlo UNSPECIFIED Flammini, Francesco UNSPECIFIED |
| Date: | 13 December 2023 |
| Number of Pages: | 236 |
| Keywords: | Artificial Intelligence; Artificial Vision; Railway Transport; Autonomous Train Driving; Smart Maintenance; Level Crossing |
| Settori scientifico-disciplinari del MIUR: | Area 09 - Ingegneria industriale e dell'informazione > ING-INF/05 - Sistemi di elaborazione delle informazioni |
| Date Deposited: | 21 Dec 2023 06:25 |
| Last Modified: | 12 Aug 2026 05:36 |
| URI: | https://www.fedoa.unina.it/id/eprint/15613 |
Collection description
This thesis explores the adoption of Deep Learning (DL) for railway safety and maintenance applications, concentrating on solutions leveraging video and audio analysis. After a systematisation of relevant Artificial Intelligence (AI) and railway concepts, the thesis evaluates the opportunities offered by DL in autonomous trains' environment perception and non-intrusive inspections of safety-critical railway assets for maintenance purposes. Then, the thesis focuses on the main challenges concerning the integration of AI into safety-related railway applications, evaluating certifiability and technical issues. The thesis first analyses both aspects methodologically. The concept of "Grade of Intelligence" is introduced for the delineation of high-level guidelines that could support the step-wise integration of AI and DL in the development of intelligent train control systems. Then, a seven-step methodology is presented for the development of DL solutions. Hence, the thesis proceeds with the discussion of two experimental case studies exemplifying the adoption of the aforementioned methodology. The first tackles the problem of anomaly detection, i.e., the identification of unknown objects, on rail tracks. The second deals with the continuous monitoring of level crossings for predictive maintenance. These were chosen for their relevance to safety within the context of study, enabling an exploration of techniques and tools to address – or exploit – specific constraints within the application domain and the lack of suitable datasets for DL models development.
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