Automated Assessment of Overhead Wire Geometry in Overlap Sections of Electrified Indian Railways

• A low-cost 2D LiDAR–IMU–GPS system for railway overhead wires overlap monitoring. • Automated assessment of wire parameters in overlap between 2 OHE sections. • Simplistic algorithm: ROI extraction, DBSCAN clustering, longitudinal slicing. • Results validated against manual measurements with 99% accuracy. • Demonstrated field deployment on railway inspection car in Indian Railways. The rapid electrification of Indian Railways has led to widespread deployment of overhead equipment (OHE) such as contact and catenary wires, cantilevers, and supporting masts. Overlap sections, where adjacent OHE tension lengths are mechanically and electrically connected, are critical to ensuring uninterrupted power supply but are also more vulnerable to failures due to their complex multi-wire configuration. Traditional inspection methods—manual gauges, visual checks, and trolley-based systems—are labor-intensive, error-prone, and impractical for large-scale deployment across diverse railway environments. This paper proposes a low-cost, rail car roof-mounted, multi-sensor measurement system integrating a 2D LiDAR, an inertial measurement unit (IMU), and a GPS module for automated assessment of OHE overlap geometry. The system transforms sequential 2D LiDAR scans into 3D point clouds through IMU–GPS fusion, enabling robust reconstruction of overlap sections. A streamlined algorithm combining region-of-interest extraction, DBSCAN clustering, and longitudinal slicing of point clouds is applied to segment catenary and contact wires while filtering out cantilever arms and droppers. Field trials conducted on a 9 km electrified route in Indian Railways demonstrated that the system achieved accuracies of 99% relative to manual measurements, with end-to-end processing requiring approximately four minutes for 10 million points. Results from both 3-span and 4-span overlaps confirm the effectiveness of the proposed method. The study highlights a scalable, time-efficient, and cost-effective framework for automated OHE monitoring, offering significant potential to enhance reliability, safety, and commissioning efficiency in large-scale railway electrification projects.