Railway Overhead Catenary (ROC) system is critical for railways’ overall performance! ROC is a linear asset that is spread over a large geographical area. Insufficient performance of ROC has a significant impact on the overall railway operations, which leads to decreased availability and affects performance of the railway system. Prognostic and Health Management (PHM) of ROC is necessary to improve the dependability of the railway. PHM of ROC can be enhanced by implementing a data-driven approach. A data-driven approach to PHM is highly dependent on the availability and accessibility of data, data acquisition, processing and decision-support. Acquiring data for PHM of ROC can be used through various methods, such as manual inspections. Manual inspection of ROC is a time-consuming and costly method to assess the health of the ROC. Another approach for assessing the health of ROC is through condition monitoring using 3D scanning of ROC utilising LiDAR technology.Presently, 3D scanning systems like LiDAR scanners present new avenues for data acquisition of such physical assets. Large amounts of data can be collected from aerial, on-ground, and subterranean environments. Handling and processing this large amount of data require addressing multiple challenges like data collection, processing algorithms, information extraction, information representation, and decision support tools. Current approaches concentrate more on data processing but lack the maturity to support the end-to-end process. Hence, this paper investigates the requirements and proposes an architecture for a data-to-decision approach to PHM of ROC based on utilisation of LiDAR technology.

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 railway is a complex technical system of systems in a multi-stakeholder environment. The implementation of digital technologies is essential for achieving operational excellence and addressing stakeholders’ needs and requirements in relation to the railways. Digitalization is highly dependent on an appropriate digital infrastructure provided through proper information logistics, whereas cybersecurity is critical for the overall security and safety of the railway systems. However, it is important to understand the various issues and challenges presented by governance, business, and technical requirements. Hence, this paper is the first link in the chain to explore, understand, and address such requirements. The purpose of this paper is to identify aspects of distributed ledgers and to provide a taxonomy of issues and challenges to develop a secure and resilient data sharing framework for railway stakeholders.

Creation, maintenance, and update of digital twins are costly and time-consuming mechanisms. The required effort can be optimized with the use of LiDAR technologies, which support the process of collecting data related to spatial information such as location, geometry, and position. Sharing such data in multi-stakeholder environments is hindered due to competition, confidentiality, and security requirements. Multi-stakeholder environments favor the use of decentralized creation and update mechanisms with reduced data exchange. Such mechanisms are facilitated by Federated Learning, where the learning process is performed at the data owner’s location. Two case studies are presented in this paper, where LiDAR is used to extract information from industrial equipment as a part of the creation of a digital twin.

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.

Maintaining safety in underground mining operations is highly dependent on understanding the geological and geotechnical properties of the site. The creation of an underground void for mining creates instability in the rock structure which leads to deformation. Compressive strength of the rocks is supported by the tensioning of ground support such as rockbolts. Monitoring and predicting the health of the mining ground support system is essential to ensure the safety of the operation. 

A Light Detection and Ranging (LiDAR) generates point-cloud data, useful for creating applications like topographic mapping and spatial models. Extraction of rockbolt information from point-cloud data acquired from underground mine can provide complete coverage of the mine and can be used to monitor the condition of the rockbolts over a period. Extracted rockbolt information can be used for health monitoring of ground support which acts as an indicator of drift deformation resulting from geostatic pressure. Thus, this paper aims to propose an approach to extract rockbolt spatial information from point-cloud datasets collected through LiDAR-technology, to support the enablement of Prognostics and Health Management in underground mining.

In underground mining, geostatic pressure results in drift surface deformation. To ensure the the stability and safety of the mining environment, a so-called ground support systems are commonly used. Today, there are various types of ground support systems, such as rockbolts, steel sets, mesh and shotcrete, cable bolts, ground anchors etc. Ground support systems based on rockbolts are used to provide the tensioning support to the compressive strength of the rocks. Surface deformation occurring in the drift brings changes in the rockbolt protruding the surface. Monitoring and predicting the health of the ground support would assist the decision process for rehabilitation of the ground support in the drift to maintain safe working conditions. In this paper, a methodology has been proposed aimed at the quantification of deformation of underground mining drifts through point cloud processing and other data sources to support health monitoring, development of decision support tools and integration with the mine Trigger, Action, Response Plans (TARPs). The proposed methodology processes point cloud data, collected in underground mining drifts in a campaign-based manner. PCD processing focuses on i) comparison of PCD quality, ii) computation of deformation while compensating for imperfect registration, iii) comparison of rockbolt regions using Wasserstein distance. The results were used to augment the PCD to create interactive visualizations on Virtual Reality (VR) and Augmented Reality (AR) systems for off-site or on-site inspections.

Railway overhead catenary is a linear asset, and its condition assessment is a difficult task due to its spread over large area, climatic conditions, dependency of specialized equipment, and personnel at the location. One of the failure modes in railway catenary is due to interaction between cables at different voltage such as the tension cable and the reinforcement cable. This paper presents a methodology for health assessment of catenary cables with the focus on measuring the minimal distance between the tension cable and the reinforcement cable. Health assessment is carried out by extracting cable information through point cloud data processing. This paper presents a novel approach for clustering of point-cloud data with known features. The proposed approach is based on an adaptation of DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm termed as “Feature Aligned DBSCAN”, this adaptation improves the candidate evaluation capability based on point cloud features during cluster expansion stage. Finally, the paper presents a verification of the inter-cable distance measurement algorithm from laboratory data. The methodology presented in this paper enables the railway catenary health assessment process spread over large regions, reducing the time and cost requirements while improving overall safety.

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.