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.

This paper proposes a methodological approach that can be applied in practice for evaluating stakeholder dynamics and assessing projects against appropriate value propositions within an industrial maintenance project context. A conceptual framework is proposed and is demonstrated through a case analysis. It is expected that the proposed methodology, the Stakeholder Interdependent Performance Opportunities and Threats, (Stakeholder iPOT), can advance project management practice by offering a mechanism for analysing stakeholder expectations and responses to the opportunities and threats that different project events present. This study highlights the need for continued investigation not only within the context of industrial maintenance projects but also in other sectors to improve our understanding and ability to effectively manage stakeholder dynamics.

The digital technologies accompanying Industry 4.0 have ushered in a new era in the management of industrial economic systems. The concept of the digital twin is at the heart of this transformation. Stemming from the convergence of advanced data analytics, Internet of Things (IoT) technologies, and virtual modelling and domain knowledge, digital twins were conceptualized to create virtual replicas of physical assets and systems. 

In recent years, technology has played a vital role in enhancing the management of risk and reliability in engineering assets, presenting both new opportunities and unforeseen challenges. The strategic focus the modern-day managers has been on seeking transformative technological and business solutions to ensure the safe and cost-effective operation of engineering assets. These transformative technologies such as industrial Internet of Things, AI, Machine Learning, and 5G communication, offer highly effective maintenance solutions, empowering engineering managers to make informed decisions while minimizing costs. As a result, asset managers have embraced digital technologies across their assets, aiming for operational excellence. However, the journey of digital transformation must address various challenges, such as technological, business, and governance-related issues etc., to ensure the safe, reliable and sustainable operation of engineering assets. The presentation underscores the significance of digital and enabling technologies, examines associated issues and challenges, and explores future trends. It highlights the potential of enabling technologies for estimation of the remaining useful life of engineering assets and implementing maintenance strategies considering contextual information.

The strategic focus of the modern-day managers has been on seeking transformative technological and business solutions to ensure the safe and cost-effective operation of engineering assets. These transformative technologies (sometime also referred to as digital technologies) such as industrial Internet of Things, AI, Machine Learning, and 5G communication, offer highly effective solutions, empowering engineering managers to make informed decisions leading to improvement in asset performance and regulatory compliance. As a result, asset managers are embracing and deploying digital technologies across their assets, aiming for operational excellence in their operations. However, the journey of digital transformation must address various challenges, such as technological, business, and governance-related issues etc., to ensure the safe, reliable, and sustainable operation of engineering assets. This paper is exploratory in nature and underscores the significance of digital and enabling technologies, examines associated issues and challenges, and also explores the future trends. It also presents short discussion on RAMS (Reliability, Availability, Maintainability and Safety) and PHM (Prognostics and Health management) in the context of industrial asset management to mitigate risks from asset operations with deployment of new technologies. 

This book provides insights into how to approach and utilise data science tools, technologies, and methodologies related to artificial intelligence (AI) in industrial contexts. It explains the essence of distributed computing and AI technologies and their interconnections. It includes descriptions of various technology and methodology approaches and their purpose and benefits when developing AI solutions in industrial contexts. In addition, this book summarises experiences from AI technology deployment projects from several industrial sectors. Features:

• Presents a compendium of methodologies and technologies in industrial AI and digitalisation.

• Illustrates the sensor-to-actuation approach showing the complete cycle, which defines and differentiates AI and digitalisation.

• Covers a broad range of academic and industrial issues within the field of asset management.

• Discusses the impact of Industry 4.0 in other sectors.

• Includes a dedicated chapter on real-time case studies.

This book is aimed at researchers and professionals in industrial and software engineering, network security, AI and machine learning (ML), engineering managers, operational and maintenance specialists, asset managers, and digital and AI manufacturing specialists.