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Liquid cargo has a significant impact on the interface wheel-track interaction. The sloshing affects the track-railcar's dynamic behavior. The effect is detectable at the track level and at the vehicle. Its characterization requires the combination of different techniques:
Empirical Mode Decomposition (EMD)
Time-Frequency Maps
Sloshing occurred when significant perturbations happened in the railcar:
The railcar entered a curve
Reduction in its travel speed
A railcar with a tank is a clear example of a multibody dynamic system with a nonlinear behavior.
This paper presents the study of the impact caused by a liquid Cargo on a railway infrastructure. The dynamic behavior of a tank car corresponds to a multibody dynamic system with several degrees of freedom. This study’s data were obtained from a scale experimental fixture consisting of a track and a railcar with a tank. The track was instrumented with strain gauges and the railcar with accelerometers. The data showed non-periodic and periodic terms; therefore, the results were analyzed with the Empirical Mode Decomposition method (EMD).
It was found that the EMD identified the signal components that were related to the sloshing. These components represent the mode shapes of the original signal.
The location of the sloshing in the track was found applying spectrograms to the accelerometer data. This paper’s experimental outputs suggest that the sloshing effect is detectable at the track and in the vehicle dynamics.
Scaled-down experimental facility: Can reproduce actual failures; can produce simulated data for identifying cracks in the substructure or other defects. The acceleration data can be converted into an equivalent deformation. The experimental facility can analyse dynamic loads. Equivalent force data: Facilitates the application to a health monitoring system; simplifies the development of predictive maintenance. These data is helpful in validating analytical or simulation models.
This paper presents a method for processing acceleration data registered on a train and determining the health condition of a subway’s substructure. The acceleration data was converted into a dynamic deformation by applying a transfer function defined using the Empirical Mode Decomposition Method.
The transfer function was constructed using data produced on an experimental rig, and it was scaled to an existing subway system. The equivalent deformation improved the analysis of the dynamic loads that affect the substructure of the subway tracks because it is considered the primary load that acts on the track and substructure. The acceleration data and the estimated deformations were analyzed with the Continues Wavelet Transform. The equivalent deformation data facilitated the application of a health monitoring system and simplified the development of predictive maintenance programs for the subway or railroad operators. This method better identified cracks in the substructure than using the acceleration data.