| 1. |
- Bellini, Emanuele, et al.
(författare)
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Resilience learning through self adaptation in digital twins of human-cyber-physical systems
- 2021
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Ingår i: Proceedings of the 2021 IEEE International Conference on Cyber Security and Resilience (CSR). - : IEEE. - 9781665402859 - 9781665402866 ; , s. 168-173
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Konferensbidrag (refereegranskat)abstract
- Human-Cyber-Physical-Systems (HPCS), such as critical infrastructures in modern society, are subject to several systemic threats due to their complex interconnections and interdependencies. Management of systemic threats requires a paradigm shift from static risk assessment to holistic resilience modeling and evaluation using intelligent, data-driven and run-time approaches. In fact, the complexity and criticality of HCPS requires timely decisions considering many parameters and implications, which in turn require the adoption of advanced monitoring frameworks and evaluation tools. In order to tackle such challenge, we introduce those new paradigms in a framework named RESILTRON, envisioning Digital Twins (DT) to support decision making and improve resilience in HCPS under systemic stress. In order to represent possibly complex and heterogeneous HCPS, together with their environment and stressors, we leverage on multi-simulation approaches, combining multiple formalisms, data-driven approaches and Artificial Intelligence (AI) modelling paradigms, through a structured, modular and compositional framework. DT are used to provide an adaptive abstract representation of the system in terms of multi-layered spatially-embedded dynamic networks, and to apply self-adaptation to time-warped What-If analyses, in order to find the best sequence of decisions to ensure resilience under uncertainty and continuous HPCS evolution.
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| 2. |
- Bernardi, Simona, et al.
(författare)
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Model-driven availability evaluation of railway control systems
- 2011
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Ingår i: Computer Safety, Reliability, and Security. SAFECOMP 2011. - Berlin, Heidelberg : Springer. - 9783642242694 ; , s. 15-28
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Konferensbidrag (refereegranskat)abstract
- Maintenance of real-world systems is a complex task involving several actors, procedures and technologies. Proper approaches are needed in order to evaluate the impact of different maintenance policies considering cost/benefit factors. To that aim, maintenance models may be used within availability, performability or safety models, the latter developed using formal languages according to the requirements of international standards. In this paper, a model-driven approach is described for the development of formal maintenance and reliability models for the availability evaluation of repairable systems. The approach facilitates the use of formal models which would be otherwise difficult to manage, and provides the basis for automated models construction. Starting from an extension to maintenance aspects of the MARTE-DAM profile for dependability analysis, an automated process based on model-to-model transformations is described. The process is applied to generate a Repairable Fault Trees model from the MARTE-DAM specification of the Radio Block Centre - a modern railway controller. © 2011 Springer-Verlag.
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| 3. |
- Besinovic, Nikola, et al.
(författare)
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Artificial Intelligence in Railway Transport : Taxonomy, Regulations, and Applications
- 2022
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Ingår i: IEEE transactions on intelligent transportation systems (Print). - : IEEE. - 1524-9050 .- 1558-0016. ; 23:9, s. 14011-14024
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Tidskriftsartikel (refereegranskat)abstract
- Artificial Intelligence (AI) is becoming pervasive in most engineering domains, and railway transport is no exception. However, due to the plethora of different new terms and meanings associated with them, there is a risk that railway practitioners, as several other categories, will get lost in those ambiguities and fuzzy boundaries, and hence fail to catch the real opportunities and potential of machine learning, artificial vision, and big data analytics, just to name a few of the most promising approaches connected to AI. The scope of this paper is to introduce the basic concepts and possible applications of AI to railway academics and practitioners. To that aim, this paper presents a structured taxonomy to guide researchers and practitioners to understand AI techniques, research fields, disciplines, and applications, both in general terms and in close connection with railway applications such as autonomous driving, maintenance, and traffic management. The important aspects of ethics and explainability of AI in railways are also introduced. The connection between AI concepts and railway subdomains has been supported by relevant research addressing existing and planned applications in order to provide some pointers to promising directions.
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| 4. |
- De Donato, Lorenzo, et al.
(författare)
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Artificial intelligence in railways : Current applications, challenges, and ongoing research
- 2023
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Ingår i: Handbook on Artificial Intelligence and Transport. - : Edward Elgar Publishing. - 9781803929545 - 9781803929538 ; , s. 249-283
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Bokkapitel (refereegranskat)abstract
- This chapter presents applications, challenges, and opportunities for the integration of artificial intelligence in rail transport, based on the current results of the European project Roadmaps for AI integration in the rail sector (RAILS). Past and ongoing research directions are briefly outlined, and then the regulatory landscape is presented as well as the main barriers to overcome. Some technical aspects are addressed to provide some valuable references, and a high-level description of ongoing research work is given, spanning from innovative studies on smart maintenance, collision avoidance, delay prediction, and incident attribution analysis to visionary scenarios such as intelligent control and virtual coupling.
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| 5. |
- De Donato, Lorenzo, et al.
(författare)
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Intelligent detection of warning bells at level crossings through deep transfer learning for smarter railway maintenance
- 2023
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Ingår i: Engineering applications of artificial intelligence. - : Elsevier Ltd. - 0952-1976 .- 1873-6769. ; 123
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 6. |
- De Donato, Lorenzo, et al.
(författare)
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Towards AI-assisted digital twins for smart railways : preliminary guideline and reference architecture
- 2023
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Ingår i: Journal of Reliable Intelligent Environments. - : Springer Science and Business Media Deutschland GmbH. - 2199-4668 .- 2199-4676.
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Tidskriftsartikel (refereegranskat)abstract
- In the last years, there has been a growing interest in the emerging concept of digital twins (DTs) among software engineers and researchers. DTs not only represent a promising paradigm to improve product quality and optimize production processes, but they also may help enhance the predictability and resilience of cyber-physical systems operating in critical contexts. In this work, we investigate the adoption of DTs in the railway sector, focusing in particular on the role of artificial intelligence (AI) technologies as key enablers for building added-value services and applications related to smart decision-making. In this paper, in particular, we address predictive maintenance which represents one of the most promising services benefiting from the combination of DT and AI. To cope with the lack of mature DT development methodologies and standardized frameworks, we detail a workflow for DT design and development specifically tailored to a predictive maintenance scenario and propose a high-level architecture for AI-enabled DTs supporting such workflow.
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| 7. |
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| 8. |
- Dirnfeld, Ruth, et al.
(författare)
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Integrating AI and DTs : challenges and opportunities in railway maintenance application and beyond
- 2024
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Ingår i: Simulation (San Diego, Calif.). - : Sage Publications. - 0037-5497 .- 1741-3133. ; 100:9, s. 903-917
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Tidskriftsartikel (refereegranskat)abstract
- In the last years, there has been a growing interest in the emerging concept of digital twin (DT) as it represents a promising paradigm to continuously monitor cyber-physical systems, as well as to test and validate predictability, safety, and reliability aspects. At the same time, artificial intelligence (AI) is exponentially affirming as an extremely powerful tool when it comes to modeling the behavior of physical assets allowing, de facto, the possibility of making predictions on their potential evolution. However, despite the fact that DTs and AI (and their combination) can act as game-changing technologies in different domains (including the railways), several challenges have to be faced to ensure their effectiveness, especially when dealing with safety-critical systems. This paper provides a narrative review of the scientific literature on DTs for railway maintenance applications, with a special focus on their relationship with AI. The aim is to discuss the opportunities the integration of these two technologies could open in railway maintenance applications (and beyond), while highlighting the main challenges that should be overcome for its effective implementation.
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| 9. |
- Dirnfeld, Ruth, et al.
(författare)
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Low-Power Wide-Area Networks in Intelligent Transportation : Review and Opportunities for Smart-Railways
- 2020
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Ingår i: 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020. - : IEEE. - 9781728141497 - 9781728141503 ; , s. 1-7
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Konferensbidrag (refereegranskat)abstract
- Technology development in the field of the Internet of Things (IoT) and more specifically in Low-Power Wide-Area Networks (LPWANs) has enabled a whole set of new applications in several fields of Intelligent Transportation Systems. Among all, smart-railways represents one of the most challenging scenarios, due to its wide geographical distribution and strict energy-awareness. This paper aims to provide an overview of the state-of-the-art in LPWAN, with a focus on intelligent transportation. This study is part of the RAILS (Roadmaps for Artificial Intelligence integration in the raiL Sector) research project, funded by the European Union under the Shift2Rail Joint Undertaking. As a first step to meet its objectives, RAILS surveys the current state of development of technology enablers for smart-railways considering possible technology transfer from other sectors. To that aim, IoT and LPWAN technologies appear as very promising for cost-effective remote surveillance, monitoring and control over large geographical areas, by collecting data for several sensing applications (e.g., predictive condition-based maintenance, security early warning and situation awareness, etc.) even in situations where power supply is limited (e.g., where solar panels are employed) or absent (e.g., installation on-board freight cars). © 2020 IEEE.
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| 10. |
- Donato, Lorenzo De, et al.
(författare)
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A Survey on Audio-Video Based Defect Detection Through Deep Learning in Railway Maintenance
- 2022
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Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 10, s. 65376-65400
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Tidskriftsartikel (refereegranskat)abstract
- Within Artificial Intelligence, Deep Learning (DL) represents a paradigm that has been showing unprecedented performance in image and audio processing by supporting or even replacing humans in defect and anomaly detection. The railway sector is expected to benefit from DL applications, especially in predictive maintenance applications, where smart audio and video sensors can be leveraged yet kept distinct from safety-critical functions. Such separation is crucial, as it allows for improving system dependability with no impact on its safety certification. This is further supported by the development of DL in other transportation domains, such as automotive and avionics, opening for knowledge transfer opportunities and highlighting the potential of such a paradigm in railways. In order to summarize the recent state-of-the-art while inquiring about future opportunities, this paper reviews DL approaches for the analysis of data generated by acoustic and visual sensors in railway maintenance applications that have been published until August 31st, 2021. In this paper, the current state of the research is investigated and evaluated using a structured and systematic method, in order to highlight promising approaches and successful applications, as well as to identify available datasets, current limitations, open issues, challenges, and recommendations about future research directions.
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