Vibration-Based Approach to Measure Rail Stress: Modeling and First Field Test

Abstract

This paper describes a non-invasive inspection technique for the estimation of longitudinal stress in continuous welded rails (CWR) to infer the rail neutral temperature (RNT), i.e., the temperature at which the net longitudinal force in the rail is zero. The technique is based on the use of finite element method (FEM), vibration measurements, and machine learning (ML). FEM is used to model the relationship between the boundary conditions and the longitudinal stress of any given CWR to the vibration characteristics (mode shapes and frequencies) of the rail. The results of the numerical analysis are used to train a ML algorithm that is then tested using field data obtained by an array of accelerometers polled on the track of interest. In the study presented in this article, the proposed technique was proven in the field during an experimental campaign conducted in Colorado. A commercial FEM software was used to model the rail track as a short rail segment repeated indefinitely and under varying boundary conditions and stress. Three datasets were prepared and fed to ML models developed using hyperparameter search optimization techniques and k-fold cross validation to infer the stress or the RNT. The frequencies of vibration were extracted from the time waveforms obtained from two accelerometers temporarily attached to the rail. The results of the experiments demonstrated that the success of the technique is dependent on the accuracy of the model and the ability to properly identify the modeshapes. The results also proved that the ML was also able to predict successfully the neutral temperature of the tested rail by using only a limited number of experimental data for the training.

Vibration-based SHM/NDE of continuous welded rails

“…This study summarizes the results extensively presented recently in [10] about a vibration-based approach supported by artificial intelligence and finite element modeling to non-invasively inspect the rails and provide reliable estimates of the neutral temperature in continuous welded rails. Specifically, the technique was tested in the field.…”

“…The technique is based on the measurement of the first few lowest modes of vibration of the rail of interest, and the use of machine learning (ML) to predict the neutral temperature from the spectral densities of the induced vibrations. This article summarizes what presented in [10], which in turn followed [11][12]. An enhanced finite element modeling was created to model the interdependency of the modal characteristics of CWR to axial stress, rail geometry, and the restraints provided by crossties, ballast, and fasteners.…”

“…For the sake of space, the detailed implementation of the FEM cannot be described here and the interested readers are referred to [10]. The CWR was "replaced" with an "equivalent" single beam segment of finite length.…”

Using Detailing Concept to Assess Railway Functional Safety

“…Energy efficiency, renewable energies, redesign of production cycles, and a modal shift towards environmentally friendly [36] means of transport are necessary measures for the transport sector [37][38][39][40][41]. In terms of energy and emissions, rail transport is among the most energy-efficient and lowest-emission transport systems.…”

Stress Evaluation in Rails Based on Vibration Data and Artificial Intelligence

“…The method was proven in the lab using a short AREMA 132 rail segment ( 18 , 19 ). Impact-driven rail vibrations were also investigated by Belding et al to evaluate axial stress in CWR sitting on concrete ties using field test data ( 20 ).…”