As part of a research that led to the development of the performance-based track geometry (PBTG) inspection technology, the Transportation Technology Center, Inc., Pueblo, CO, USA, a wholly owned subsidiary of the Association of American Railroads, has developed a technique relating track geometry to vehicle performance, real-time. This technique is based on the neural network (NN) approach, an emerging powerful tool in recognizing complex patterns and non-linear relationships between many inputs and an output, such as the relationship between track geometry and vehicle response. On the basis of this technique, many NNs have been developed (trained) from actual vehicle/track interaction test results. For a given vehicle type, the trained NNs directly relate three-dimensional track geometry and vehicle operating speed to vehicle performance. The effects of other track conditions such as lubrication, rail profile, and track stiffness are indirectly considered on the basis of their statistical distributions from test results. Because the PBTG inspection is intended to optimize the track geometry maintenance, the vehicle types selected for testing to date were the ones most sensitive to track geometry (in North America, these are the tank car, covered hopper car, and coal gondola car). However, more NNs for other vehicle types can be easily trained on the basis of the NN technique developed using actual vehicle performance and track geometry test results.
This study explores detecting rail defects (track ride quality exceptions) during train operations using real-time x, y and z acceleration data measured and collected on the side frame of a 315,000-lb gross rail load instrumented freight car. Different analysis tools were developed and employed to capture the peculiar characteristics of the data. This includes Harmonic analysis of the data, Wavelet analysis, energy density analysis, and correlation analysis. Based on the analysis results, different filtration and processing techniques were tried to identify the defects throughout the test data. A method that augments autocorrelation to wavelet based singularity detection showed promising results in capturing three types of exceptions: rail fractures, chipped rails, and broken concrete foundations. In addition, blind tests were conducted with several datasets and the algorithm proved to be 100% accurate in detecting the studied defects.
Track conditions can change rapidly when operating in a revenue service heavy axle load environment. Because the assessment of track health for safe railroad operations is a fundamental priority for the railroad industry in North America, various track monitoring technologies, such as Track Geometry Measurement Vehicles (TGMVs) and unattended Vehicle/Track Interaction (V/TI) systems, are regularly deployed to determine whether track maintenance attention is warranted. To investigate the track condition monitoring differences between TGMV and V/TI technologies in a controlled environment where rapid tonnage is accumulated, Transportation Technology Center, Inc. (TTCI) engineers conducted a study on the High Tonnage Loop (HTL) at the Facility for Accelerated Service Testing (FAST) in Pueblo, CO. This study used TTCI’s rail-bound TGMV and a TTCI-developed Instrumented Freight Car (IFC), an autonomous car-based V/TI monitoring technology designed to run with the FAST train for continuous track condition assessment in a heavy axle load environment. In this investigation, funded by the Association of American Railroads (AAR) Strategic Research Initiatives (SRI) program, a total of six TGMV and IFC tests were conducted at different intervals with over 87.4 million gross tons (MGT) accumulated since the first track geometry baseline test was conducted. Compliance with track geometry standards was determined based on the Federal Railroad Administration (FRA) regulatory geometry defects for track Classes 1 through 5. Exception track locations were identified for the IFC when empirically derived, pre-set performance limits were exceeded. This paper both examines and contrasts the findings of the testing results of both the IFC and TGMV technologies as studied at FAST.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.