Proceedings of the

The 33rd European Safety and Reliability Conference (ESREL 2023)
3 – 8 September 2023, Southampton, UK

Condition Monitoring of Railway Overhead Catenary through Point Cloud Processing

Amit Patwardhana, Adithya Thadurib, Ramin Karimc and Miguel Castano Arranzd

Division of Operation and Maintenance, Luleå University of Technology, Sweden.


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.

Keywords: Railway overhead catenary, LiDAR, Point cloud, Digital twin.

Download PDF