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).