Railway has the potential to become one of the most sustainable mediums for passenger and freight transport. This is possible by continuous updates to the asset management regime supporting Prognostics and Health Management (PHM). Railway tracks and catenaries are linear assets, and their length plays a vital role in maintenance. Railway catenary does not present many failures as compared to the rail track, but the failures that occur do not give enough opportunity for quick recovery. These failures cause extensive time delays disrupting railways operations. Such situations can be handled better by updating the maintenance approach. The domain of maintenance explores possible tools, techniques, and technologies to retain and restore the systems. PHM is dependent on data acquisition and analytics to predict the future state of a system with the least possible divergence. In the case of railway catenary and many other domains, this new technology of data acquisition is Light Detection And Ranging (LiDAR) device-based spatial point cloud collection. Current methods of catenary inspection depend on contact-based methods of inspection of railway catenary and read signals from the pantograph and contact wire while ignoring the rest of the wires and surroundings. Locomotive-mounted LiDAR devices support the collection of spatial data in the form of point-cloud from all the surrounding equipment and environment. This point cloud data holds a large amount of information, waiting for algorithms and technologies to harness it. A Digital Twin (DT) is a virtual representation of a physical system or process, achieved through models and simulations and maintains bidirectional communication for progressive enrichment at both ends. A systems digital twin is exposed to all the same conditions virtually. Such a digital twin can be used to provide prognostics by varying factors such as time, malfunction in components of the system, and conditions in which the system operates. Railways is a multistakeholder domain that depends on many organisations to support smooth function. The development of digital twins depends on the understanding of the system, the availability of sensors to read the state and actuators to affect the system’s state. Enabling a digital twin depends on governance restrictions, business requirements and technological competence. A concrete step towards enablement of the digital twin is designing an architecture to accommodate the technical requirements of content management, processing and infrastructure while addressing railway operations' governance and business aspects.The main objective of this work is to develop and provide architecture and a platform for the enablement of a DT solution based on Artificial Intelligence (AI) and digital technologies aimed at PHM of railway catenary system. The main results of this thesis are i) analysis of content management and processing requirements for railway overhead catenary system ii) methodology for catenary point cloud data processing and information representation iii) architecture and infrastructure requirements for enablement of Digital Twin and iv) roadmap for digital twin enablement for PHM of railway overhead catenary system.

Railways are a main element of a sustainable transport policy in several countries as they are considered a safe, efficient and green mode of transportation. Owing to these advantages, there is a cumulative request for the railway industry to increase the performance, the capacity and the availability in addition to safely transport goods and people at higher speeds. To meet the demand, large adjustment of the infrastructure and improvement of maintenance process are required.  Inspection activities are essential in establishing the required maintenance, and it is periodically required to reduce unexpected failures and to prevent dangerous consequences.  Maintenance of railway catenary systems is a critical task for warranting the safety of electrical railway operation.Usually, the catenary inspection is performed manually by trained personnel. However, as in all human-based inspections characterized by slowness and lack of objectivity, might have a number of crucial disadvantages and potentially lead to dangerous consequences. With the rapid progress of artificial intelligence, it is appropriate for computer vision detection approaches to replace the traditional manual methods during inspections.  In this thesis, a strategy for monitoring the health of catenary wires is developed, which include the various steps needed to detect anomalies in this component. Moreover, a solution for detecting different types of wires in the railway catenary system was implemented, in which a deep learning framework is developed by combining the Convolutional Neural Network (CNN) and the Region Proposal Network (RPN).