This paper explores the potential of Metaverse technology in industrial Asset Management (AM). By integrating AI and digital technologies, the Metaverse can enhance Human-System-Interaction (HSI) and optimise AM processes. However, implementing a Metaverse in industrial contexts faces challenges, particularly in visualising physical and virtual assets. This paper conducts a systematic review to address these challenges and identify potential solutions. The findings reveal that while the necessary technologies are available, their widespread adoption in industrial AM is limited. The paper presents a comprehensive overview of research themes related to Metaverse applications in industrial contexts, highlighting the evolving landscape and potential benefits. Ultimately, this research aims to contribute to the advancement of Metaverse technology in industrial AM by providing insights into its development, implementation, and challenges along with an Industrial Metaverse Framework. An example of applying the Metaverse concept in the railway sector has been presented and validated using railway digital assets available within the eMaintenance LAB. The practical implications of this work are expected to result in increased efficiency and effectiveness in the operation and maintenance procedures across various industrial sectors.

Digitalisation within industries has many posi- tive opportunities but poses many cybersecurity-related threats. Cybersecurity is a critical concern for many indus- tries, such as railway, aviation, mining, construction, health- care, and finance, where vital information and operational security are at risk of being compromised. Today, research- ers are looking into various solutions to tackle cybersecurity risks while still retaining the desired functionality of the system. However, it is believed that these challenges can be approached by integrating game theory and the cyber kill chain (CKC), which describes the different stages of a cyberattack, to understand the complexities of cybersecu- rity. Thus, the primary objective of this paper is to demon- strate the application of a strategic game model within the context of cybersecurity, specifically using the cyber kill chain (CKC) model. The focus is on validating the proposed strategic game model through a case study. The case study involves a scenario where defenders select strategies such as “monitor system,” “detecting system,” and “respond to attack,” while attackers choose various attack strategies like “monitoring attack,” “scan the organization’s website,” and “develop malicious payloads” within the stages of the CKC. This approach aims to enhance understanding of the complex challenges and facilitate the development of effective cyber- security solutions. This approach will help in evaluating the effectiveness of different security strategies. The proposed strategic approach uses a non-cooperative game based on mixed strategies. The authors have defined a scenario for simultaneous-move games by estimating values for various elements of the game. By analysing the behaviour of both attacker and defender, the proposed game-based model can help industries develop more effective and efficient security strategies. Further, the proposed model will provide a better understanding of the complex challenges of cybersecurity in industrial contexts. It can also be used to develop appropri- ate strategies to mitigate cybersecurity risks.

Industry 5.0 promises to revolutionize the industry by focusing on human-centric, sustainability, and resilience empowered by emerging technologies such as Artificial Intelligence (AI) and digitalization. This paradigm shift is expected to bring significant advancements in sustainability, resilience, productivity, effectiveness, efficiency, customization, reliability, safety, security, maintainability etc. However, this shift of the industrial paradigm introduces substantial cybersecurity challenges due to the increased attack surface and data sensitivity. Therefore, the objective of this paper is to conduct a thorough literature review of the recent research on cybersecurity in Industry 5.0, highlighting emerging trends, gaps, and potential solutions. To conduct this research, the authors have applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to investigate cybersecurity solutions in Industry 5.0. The findings reveal that conceptual research dominates, with AI, Blockchain, and Internet of Things (IoT) most prevalent but highlights a gap in linking cybersecurity to resilience and sustainability. Furthermore, the paper aims to present trends in cybersecurity research with more relevant results from 2022 to 2024. It conducts a thorough review of the literature, highlighting the evolving landscape of cybersecurity applications in Industry 5.0.

Digitalisation is transforming the railway globally. One of the major considerations in digital transformation of any industry including the railway is the increased exposure to cyberattacks. The railway industry is vulnerable to these attacks because since the number of digital items and also number of interfaces between digital and physical components in the railway systems keep increasing. Increased number of items and interfaces require new frameworks, concepts and architectures to ensure the railway system’s resilience with respect to cybersecurity challenges, such as lack of proactiveness, lack of holistic perspective and obsolescence of safety systems exposed to current and future cyber threats landscape. To this date, there are several works carried out in the literature that studied the cybersecurity aspects and its application on railway infrastructure. However, to develop and implement an appropriate roadmap to cybersecurity in railways, there is a need of describing emerging challenges, and approaches to deal with these challenges and the possibilities and benefits of these.Hence, the objective of this paper is to provide a systematic review and outline cybersecurity emerging trends and approaches, and also to identify possible solutions by querying literature, academic and industrial, for future directions. The authors of this paper conducted separate searches through four popular databases, that is, Google Scholar, Scopus, Web of Science and IEEE explore. For the screening process, authors have used keywords with Boolean operators and database filters and identified 90 articles most relevant to the study domain. The analysis of 90 articles shows that majority of the cybersecurity studies lies within the railways are conceptual and lags in application of Artificial Intelligence (AI) based security. Like other industries, it is very important that railways should also follow latest security technologies, trends and train their workforce for cyber hygiene since railways are already in digitalization transition mode.

The health of mobile mining machinery is critical to the achieve effectiveness and efficiency in mining production. However, the performance of mobile mining machinery, such as dumpers, is influenced by factors such as the operational environment, machine reliability, maintenance regime, human factor, etc., that lead to the downtime of dumpers. These downtimes have significant consequences on the overall equipment effectiveness (OEE) and lead to decreased capacity, increased maintenance costs and reduces availability. The enablement of prognostics and health management (PHM) can contribute to improve the OEE in mining production.

Conventionally, the existing solutions focus mostly on the reliability and maintainability analysis of dumpers using failure data, maintenance data, operation data etc. Though several existing methods utilize condition monitoring techniques, there is less focus on monitoring the engine vibration and impact the health of the driver. In addition, the existing solutions are not real-time, scalable, or offline-based. Hence, the objective of this paper is to develop a concept for the enablement of PHM for the engine and driver comfort of dumpers. Furthermore, a cloud-based solution for condition monitoring of dumpers has been designed and developed. The solution can be used to assess the engine vibrations and seat vibrations and to estimate the remaining useful life (RUL) of the selected features using standards. The cloud-based architecture is implemented on the AI Factory platform that enable PHM for the improvement of OEE. This platform also facilitates the enablement of a digital twin for components and systems within dumpers or other mobile mining machinery.

The ongoing digitalisation of industrial systems is bringing new challenges in managing, monitoring, and predicting the overall reliability performance. The overall reliability of a cyber-physical system, such as railways, is highly influenced by the level of resilience in its inherent digital items. The objective of this paper is to propose a systematic approach, based on an enhanced Cyber Kill Chain model, to improve the overall system resilience through monitoring and prediction. The proposed cybersecurity approach can be used to assess the future cyberattack penetration probabilities based on the present security controls. With the advancement in cybersecurity defensive controls, cyberattacks have continued to evolve through the exploitation of vulnerabilities within the cyber-physical systems. Assuming the possibility of a cyberattack it is necessary to select appropriate security controls so that this attack can be predicted, prevented, or detected before any catastrophic consequences to retain the resilience of the system. Insufficient cybersecurity in the context of cyber-physical systems, such as railways, might have a fatal effect on the whole system availability performance and sometimes may lead to safety risks. However, to improve the overall resilience of a cyber-physical system there is a need of a systematic approach to continuously monitor, predict, and manage the health of the system’s digital items with respect to security. Furthermore, the paper will provide a case-study description in railway sector, which has been used for the verification of the proposed approach.

The ongoing digital transformation is changing asset management in the railway industry. Emerging digital technologies and Artificial Intelligence is expected to facilitate decision-making in management, operation, and maintenance of railway by providing an integrated data-driven and model-driven solution. An important aspect when developing decision-support solutions based on AI and digital technology is the users’ experience. User experience design process aims to create relevance, context-awareness, and meaningfulness for the end-user. In railway contexts, it is believed that applying a human-centric design model in the development of AI-based artefacts, will enhance the usability of the solution, which will have a positive impact on the decision-making processes. In this research, the applicability of such advanced technologies i.e., Virtual Reality, Mixed Reality, and AI have been reviewed for the railway asset management. To carry out this research work, literature review has been conducted related to available Virtual Reality/Augmented Reality/Mixed Reality technologies and their applications within railway industry. It has been found that these technologies are available, but not applied in railway asset management. Thus, the aim of this paper is to propose a human-centric design model for the enhancement of railway asset management using Artificial Intelligence, Virtual Reality, and Mixed Reality technologies. The practical implication of the findings from this work will benefit in increased efficiency and effectiveness of the operation and maintenance processes in railway