Evaluating Rail Surface Roughness from Axle-Box Acceleration Measurements: Computational Metrology Approach
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 147, Issue 12
Abstract
This study develops a new methodology based on computational metrology techniques to measure rail surface roughness from vertical axle-box accelerations. A VIA rail passenger rail car, operating in eastern Ontario, Canada, was instrumented with accelerometers on the car body, trucks, and axle-boxes. The rail surface was measured by a heavy track geometry inspection car. A Gaussian filter was applied to the measured rail surface data and the rail surface roughness was quantified by the root mean square (RMS), as recommended by ISO 11562 for evaluating engineering surfaces. Values of rail surface RMS roughness calculated from axle-box accelerometer data are verified with the measured surface RMS roughness. The overlap ratio and length of the moving window over which the rail surface RMS roughness is calculated are studied with respect to roughness wavelengths, statistical considerations, and maintenance planning purposes. The effect of rail car operating speed as well as the difference between axle-box accelerations measured at two axles of the instrumented rail car on the accuracy of estimating the rail surface RMS roughness are assessed. Filtering techniques and application limitations for calculating roughness are also discussed. The results of this study suggest axle-box acceleration data provide a useful assessment of rail surface roughness for the typical wavelengths between 3 and 25 m and a complementary technique to light and heavy track geometry inspections.
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Data Availability Statement
Acceleration and geometry data used during the study were provided by the National Research Council Canada (NRC). Direct requests for these materials may be made to the NRC. All GPS coordinate data used during the study are confidential in nature and may only be provided with restrictions.
Acknowledgments
The authors thank the National Research Council Canada (NRC) for motivating this research project, for sharing collected data with the Canadian Railway Research Laboratory (CaRRL) as part of a collaborative research initiative, and for partial funding for the research. The authors also gratefully acknowledge the collaboration of VIA Rail Canada Inc., particularly assistance provided by Paul Charbachi. Special thanks are also extended to NRC’s AST testing and evaluation team for installing and maintaining the instrumentation during the initial NRC/VIA data collection project. This research was made possible through the CaRRL research program at the University of Alberta, which is funded by Canadian National Railway (CN), Transport Canada, NRC AST, and the Natural Sciences and Engineering Research Council of Canada (Grant No. IRC 523369-18).
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Journal of Transportation Engineering, Part A: Systems
Volume 147 • Issue 12 • December 2021
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© 2021 American Society of Civil Engineers.
History
Received: Apr 21, 2021
Accepted: Jul 30, 2021
Published online: Sep 24, 2021
Published in print: Dec 1, 2021
Discussion open until: Feb 24, 2022
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