Industrial cybersecurity has become a critical concern in today's interconnected world, as criticalinfrastructure systems increasingly rely on digital technologies. This paper explores the unique challenges andopportunities presented by industrial cybersecurity, highlighting the need for enhanced cybersecurity measures.The paper discusses the potential consequences of cyberattacks on industrial systems, including disruptions tocritical services, economic losses, and even physical harm. To address these challenges, this paper discussescybersecurity initiatives, standards, guidelines, directives, and acts that can provide a comprehensive framework forcybersecurity and AI governance. A systematic literature review has been conducted in this paper using Scopus andGoogle Scholar, which provide the foundation for identifying relevant publications. These publications show keytrends and themes in industrial cybersecurity research, including the growing importance of education and training, aswell as cybersecurity risk assessment and mitigation.

Industrial assets have become increasingly complex to support the requirements of quality, productivity, and cost-effectiveness. The industrial needs and requirements to effectively and efficiently operate, maintain and manage the complex technical industrial assets have propelled the advancement of technology. Digitalisation has been one of the significant enablers for operating, maintaining, and managing such complex technical assets.

The operations of an organisation are significantly influenced by asset management. It is characterised as the means through which an organisation can derive value from its assets to meet its goals. When managing complex technical System-of-Systems, maintenance plays an essential role to ensure that the delivered function of the system fulfils the requirements.

An efficient maintenance process helps detect potential problems early, preventing them from becoming significant failures and reducing costly downtime. By keeping assets in optimal condition, organisations can enhance reliability and performance, which is crucial for achieving business and operational objectives, as well as meeting regulatory requirements.

Traditional maintenance planning methods are inadequate for linear assets because of their extended lifespan and varying conditions. A more effective approach is needed to address RAMS, criticality, resilience, and sustainability cost-effectively throughout the asset's lifespan.

Linear assets refer to infrastructure that spans over large geographical areas, such as high-tension power cables, railway overhead catenary, pipelines, highways, and underground mining drifts. These assets are difficult to maintain and often lack a comprehensive digital footprint due to absence of appropriate sensors and data processing techniques. This research aims to address these challenges by adapting techniques from cyber-physical systems and development of Digital Twins (DT) for linear assets. To manage the inherent complexity System-of-Systems approach has been employed during the development process. The primary focus of this research is on spatial condition monitoring and health management of linear assets through maintenance decisions and decision support tools, with emphasis on railway overhead catenary and underground mining drifts.

However, the advancement of Artificial Intelligence (AI) and digital technologies facilitates the creation of solutions that are anticipated to improve business processes, asset management, and the operation and maintenance of industries. Technological advancements, especially AI represented by Digital Twins, have the potential to revolutionise business processes, operational strategies, and maintenance practices, thereby leading to operational excellence.

Hence, the research aims to enhance the maintenance of linear assets through the development of Digital Twins (DT) empowered by digital technologies and Artificial Intelligence (AI).

This paper presents a methodology and the results of workflow developed to process point cloud data fromunderground drifts for condition monitoring of ground support. The workflow focuses on extraction andcomparison of information of individual rockbolts and rockbolt neighbourhood prior to, and following,recorded seismic events. Data sources used in this methodology are point cloud data resulting from mobileLiDAR scanning and event data of blasting and microseismic events. In the first step of the workflow, locationsin the drift with recorded microseismic events in the vicinity are selected. In the second step, LiDAR scansperformed before and after the occurrence of one or more natural or man-made events are used to extractpoint cloud data within a region close to the recorded events. The extracted point cloud data is processed tocompute information about the rockbolts. For each detected rockbolt, the following information is extracted:position on drift surface, tip position, angle to drift surface, length, neighbouring rockbolts, and rockbolt toneighbour’s distances. In the next phase, the rockbolt information extracted from two or more scans over theperiod encompassing the event are analysed. Corresponding rockbolt information from pre-event andpost-event point cloud data are used to compute variation in rockbolt features. The computed variations areexamined statistically and used to create a visualisation for decision support to be used by rock mechanicsengineers and surveyors.

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.

Railway overhead catenary (ROC) is a linear asset and spread over large area. Different regions of the linear asset are exposed to different climate conditions such as temperature, wind, and ice accretion and operating conditions. If these conditions disrupt the functionality, then it leads to failure resulting in line closure. Being ROC is a linear asset, condition monitoring (CM) is difficult due to large distances, climate conditions, costly due to requirement of special equipment at the location and effects the scheduled traffic by occupying the tracks. Hence, there is a need for technologies to monitor the condition of ROC through a cloud-based approach which has faster response time. Light Detection and Ranging (LiDAR) can be used for CM of ROC. It collects spatial data in the form of 3D point cloud in various domains such as construction, mining and railways. LiDAR devices will be mounted on locomotives on a regular traffic. The point cloud data is processed to extract the railway assets such as tracks, masts, catenary etc. and surrounding vegetation. Further, processing of point cloud data can be used to extract exact location and position of the assets. One of the failure modes for ROC, if the distance between the two wires is less than the specifications, then it leads to failure. This paper develops a cloud-based approach to measure the distance between specific wires, through processing of point cloud data. This approach forms the foundation for data augmentation and development of hybrid digital twins (DT) of railway overhead catenary.

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

Digitalisation is changing the railway globally. One of the major concerns in digital transformation of the railway is the increased exposure to cyberattacks. The railway is vulnerable to these cyberattacks because the number of digital items and number of interfaces between digital and physical components in these systems keep growing. Increased number of digital items and interfaces require new methodologies, frameworks, models, concepts, and architectures to ensure the railway system’s resilience with respect to cybersecurity challenges, such as adoption and convergence of Information Technology (IT) and Operational Technology (OT) technology within the railway. This convergence has brought significant benefits in reliability, operational efficiency, capacity as well as improvements in passenger experience but also increases the vulnerability towards cyberattacks from individuals, organizations, and governments. This paper proposes a methodology on how to deals with OT security in the railway signalling using failure mode, effects and criticality analysis (FMECA) and ISA/IEC 62443 security risk assessment methodologies.