Abstract
Untreated wheel surface defects can diminish railway system reliability and availability as well as deteriorate operational safety and ride quality. While wayside and trainborne detectors for wheel condition monitoring, such as wheel impact load detector, are developed, they provide limited insights into defect type and severity. Therefore, this paper aims to investigate and ascertain the association between wheel flat, wheel-rail impact responses, and vibration signals. A three-dimensional finite element model of dynamic wheel-rail interaction, along with healthy and defective wheels with flat lengths of 15 mm, 30 mm, 45 mm, and 60 mm, were developed. Results reveal strong positive linear relationships between wheel flat and von Mises stress at wheel-rail contact (R-squared = 0.9574), von Mises pressure on the rail (R-squared = 0.9154), and power spectral density magnitude of wheel vibration signal at 283 Hz (R-squared = 0.9636). These findings can serve as a solid basis for developing intelligent wayside and trainborne detectors, thereby increasing the productivity of wheel maintenance work.
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Ng, A.K., Yap, T.C. (2022). Railway Wheel Flat Modeling and Vibration Signal Analysis for Improved Wheel Condition Monitoring and Predictive Maintenance. In: Zhang, Z. (eds) 2021 6th International Conference on Intelligent Transportation Engineering (ICITE 2021). ICITE 2021. Lecture Notes in Electrical Engineering, vol 901. Springer, Singapore. https://doi.org/10.1007/978-981-19-2259-6_48
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DOI: https://doi.org/10.1007/978-981-19-2259-6_48
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