We investigate the use of impulse-based resonant vibration applied to steel rail structures to monitor axial stress conditions in rail on an in-service railway line. Continuously welded rail develops axial residual stress as the rail temperature changes because of changing ambient environmental conditions; high temperatures can cause high levels of residual axial compressive stress to develop, potentially leading to rail bucking, also called "sun kinks."  Sun kinks are a major concern to the rail industry because they are a leading cause of train derailment in the United States. Thus it is important to monitor rail stress state in rail. However, existing non-destructive test methods are unable to reliably predict rail stress state, especially without additional baseline data. In this study, vibration data are collected from specially instrumented rail where true axial stress state and temperature are continuously monitored over the long term. Resonant vibration data are collected across different temperatures and stress states using a contactless sensor. The results demonstrate that certain high-frequency modes of vibration are affected by the temperature-induced stress state in the rail, yet are largely unaffected by varying foundation and track attachment conditions. Results from finite element simulations illustrate the nature of the identified vibrational modes and illustrate how they are affected by stress and geometrical considerations. Signal processing algorithms are introduced to identify, extract, and track pertinent vibrational modes within the rich experimental vibration spectra.  Finally, the potential for these data to be used with machine learning algorithms to predict rail stress conditions, without other baseline measurements, is proposed. 


Progress in Monitoring In-Place Rail Stress Conditions Using Impulse Vibration Measurements

No proceedings papers in 2020. Video only.

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