Railway Digital Twins and Artificial Intelligence: Challenges and Design Guidelines

Dirnfeld, Ruth; Donato, Lorenzo De; Flammini, Francesco; Azari, Mehdi; Vittorini, Valeria

doi:10.1007/978-3-031-16245-9_8

Cited by 8 publications

(3 citation statements)

References 40 publications

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“…As a PoC of the approach described in the previous sections, in this section we provide a case-study that is relevant for IIoT-connected railways, which use smart devices to: deliver services to passengers and train drivers; perform automatic train control and maintenance; and control energy consumption [35]. Modern railways can highly benefit from DT technology, which allows for what-if analyses of critical situations and proactive decision-making [36].…”

Section: The Railway Iiot Proof-of-conceptmentioning

confidence: 99%

Digital Twins for Anomaly Detection in the Industrial Internet of Things: Conceptual Architecture and Proof-of-Concept

Benedictis

Flammini

Mazzocca

et al. 2023

IEEE Trans. Ind. Inf.

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Modern cyber-physical systems based on the Industrial Internet of Things (IIoT) can be highly distributed and heterogeneous, and that increases the risk of failures due to misbehavior of interconnected components, or other interaction anomalies. In this paper, we introduce a conceptual architecture for IIoT anomaly detection based on the paradigms of Digital Twins (DT) and Autonomic Computing (AC), and we test it through a proof-of-concept of industrial relevance. The architecture is derived from the current state-of-the-art in DT research and leverages on the MAPE-K feedback loop of AC in order to monitor, analyze, plan, and execute appropriate reconfiguration or mitigation strategies based on the detected deviation from prescriptive behavior stored as shared knowledge. We demonstrate the approach and discuss results by using a reference operational scenario of adequate complexity and criticality within the European Railway Traffic Management System.

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Section: The Railway Iiot Proof-of-conceptmentioning

confidence: 99%

Digital Twins for Anomaly Detection in the Industrial Internet of Things: Conceptual Architecture and Proof-of-Concept

Benedictis

Flammini

Mazzocca

et al. 2023

IEEE Trans. Ind. Inf.

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show abstract

“…Te authors in [83] discriminated the diference and relationship between conventional trafc simulation and digital twin (DT) in terms of features, functions, input data, modelling, and interaction, and they proposed three-layer technical architecture for DT in intelligent transportation, i.e., data access layer (lowest level), computational simulation layer (middle level), and application management layer (highest level). Te review in [85] showed that most of the digital twin-related publications focused on the railway subdomain maintenance, inspection, and resilience, most of which applied machine learning algorithms and techniques in digital twin to predict failures, detect faults, make automated decisions, supervise train movements, provide information on passenger behavior onboard trains, and monitor health status of railway systems.…”

Section: Papermentioning

confidence: 99%

Artificial Intelligence Applied on Traffic Planning and Management for Rail Transport: A Review and Perspective

Zhang

2023

Discrete Dynamics in Nature and Society

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Artificial intelligence (AI) has received much attention in the domain of railway traffic planning and management (TPM) from academia and industries. While many promising applications have been reported, there remains a lack of detailed review of the many AI models/algorithms and their uses and adaptations in rail TPM. To fill this gap, this systematic literature review conducts, reports, and synthesizes the state-of-the-art of AI applied in railway TPM from four perspectives, i.e., the intersection between AI research fields (e.g., expert systems, data mining, and adversarial search) and rail TPM, the intersection between AI techniques (e.g., evolutionary computing and machine learning) and rail TPM, the intersection between AI applications (e.g., operations research, scheduling, and planning) and rail TPM, and the intersection between AI related disciplines (e.g., big data analytics and digital twins) and rail TPM. The study evaluates 95 research papers published during 1970–2022. Accordingly, a comprehensive synthesis of each intersection between AI and rail TPM is presented, and the practical roadmap for application of AI in rail TPM is proposed. Furthermore, the study identifies the research gaps and areas that need more investigation. The contribution helps researchers and practitioners to get a better understanding of the status quo of research stream, research development trends, and challenges for further related study.

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“…Despite several scopes of DTR to advance the railway, few surveys have been conducted on the topic. Addressing maintenance, the authors in [11] elucidated the challenges and advantages of DT for railways, employing Machine Learning (ML) within the DT framework. Additionally, Condition-Based Maintenance (CBM) in the scope of DTR was explored in [12] and [13].…”

Section: Existing Surveysmentioning

confidence: 99%

Digital Twin for Railway: A Comprehensive Survey

Ghaboura,

Ferdousi,

Laamarti

et al. 2023

IEEE Access

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Digital transformation has been prioritized in the railway industry to bring automation to railway operations. Digital Twin (DT) technology has recently gained attention in the railway industry to fulfill this goal. Contemporary researchers argue that DT can be advantageous in Railway manufacturing logistics to planning and scheduling. Although underlying technologies of DT, e.g., modelling, computer vision, and the Internet of Things, have been studied for various railway industry applications, the DT has been least explored in the context of railways. Thus, in this paper, we aim to understand the state-of-the-art of DT for railway (DTR), for advanced railway systems. Besides, this survey clarifies how DT can serve the railway twin system designers and developers. As DTR is still in its early adoption stage, there is hardly any clear direction to identify the technologies for specific DTR applications. Therefore, based on our findings we present a taxonomy for DTR for designers and developers. Finally, we describe potential challenges, pitfalls, and opportunities in DTR for future researchers.

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Railway Digital Twins and Artificial Intelligence: Challenges and Design Guidelines

Cited by 8 publications

References 40 publications

Digital Twins for Anomaly Detection in the Industrial Internet of Things: Conceptual Architecture and Proof-of-Concept

Digital Twins for Anomaly Detection in the Industrial Internet of Things: Conceptual Architecture and Proof-of-Concept

Artificial Intelligence Applied on Traffic Planning and Management for Rail Transport: A Review and Perspective

Digital Twin for Railway: A Comprehensive Survey

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