Simulated and experimental fine damage models for steel railway tracks

Abstract The integrity of railway track structures is gravely threatened by rail damage, posing significant risks to the safe operation of railway transportation systems. However, existing detection techniques, including computer vision, ultrasound, acoustic emission, and thermal imaging, exhibit inherent limitations in identifying minor defects and concealed damage at the rail’s underside. To address this challenge, a rail damage detection method based on modal analysis is proposed, integrating finite element (FE) simulations with experimental measurements to enable the effective identification of various damage types and severities. Initially, a three-dimensional rail model is constructed using SolidWorks, and its dynamic characteristics are thoroughly analyzed through Ansys FE simulations. Subsequently, a compact micro-electromechanical system sensor array is designed and deployed to capture rail vibration signals, with actual modal properties extracted via PolyMAX software and compared against the simulation model to validate its reliability. Based on this validated model, the influence of different damage conditions on the rail’s natural frequencies is further examined. Findings reveal a pronounced correlation between damage severity and the reduction in natural frequency, with greater damage leading to a more significant decline. Effectively detecting early-stage, imperceptible rail damage, this method provides a robust technological foundation for rail structural health monitoring and holds substantial engineering applicability.