Journal of Engineering, Project, and Production Management, 2026, 16(6), 2026-0113
Wheel–Rail Resonance Dynamic Monitoring and Preventive Grinding
1 Engineer, Technology and
Informatization Management, Line Branch, Beijing Metro Operation Co.,
Ltd., 100082, Beijing, China
2 Engineer, Equipment
Maintenance Management Department, Line Branch, Beijing Metro Operation
Co., Ltd., 100082, Beijing, China
3 Engineer,
Comprehensive Maintenance Project Department No.8, Line Branch, Beijing
Metro Operation Co., Ltd., 100082, Beijing, China
4
Engineer, Comprehensive Maintenance Project Department No.7Line Branch,
Beijing Metro Operation Co., Ltd., 100082, Beijing, China
5
Engineer, Beijing Wheel-Rail Relationship Research Office, Beijing Virel
Railway Transit Technology Co., Ltd.,
Beijing, 100070, China, E-mail:
lzxz8143@outlook.com (corresponding author).
6 Engineer,
Wheel-Rail Relationship Research Office, Rolling Stock Research
Institute, China Academy of Railway Sciences Corporation Limited,
100081, Beijing, China
Engineering Management
Received July 3, 2026; revised July 30, 2026; accepted August 2, 2026
Available online September 21, 2026
Abstract: Rail corrugation-induced wheel–rail resonance is one of the most persistent challenges in urban rail transit systems, leading to excessive noise generation, accelerated infrastructure deterioration, and increased maintenance costs. Existing preventive rail grinding practices are predominantly based on geometric measurements of corrugation depth and wavelength, which often fail to accurately identify the optimal maintenance intervention period. To address this limitation, this study proposes a rail dynamic response-based wheel–rail resonance monitoring framework and investigates the dynamic evolution of rail corrugation for preventive maintenance decision-making. A continuous field monitoring system was deployed on corrugated sections of the Beijing Metro network to acquire rail vibration responses during train passages. The collected acceleration signals were analyzed using frequency-domain techniques and a resonance identification algorithm to determine the resonance frequency, vibration amplitude, and the probability of resonance occurrence. Based on long-term monitoring data, a wheel–rail resonance probability index (P) was introduced to quantitatively characterize the severity of resonance phenomena and track the progression of rail corrugation. The results revealed a strong correlation between the development of corrugation and wheel–rail resonance behavior. Type II corrugation exhibited significantly faster resonance evolution than Type I corrugation, with resonance frequencies concentrated around 400 Hz. Furthermore, resonance probability increased consistently with corrugation depth, demonstrating the effectiveness of dynamic indicators in assessing rail deterioration. Based on the observed resonance characteristics, the corrugation deterioration process was successfully divided into three dynamic phases: Dynamic Phase I (non-resonant growth stage), Dynamic Phase II (resonance development stage), and Dynamic Phase III (stable resonance stage). Based on the monitored sections investigated in this study, Dynamic Phase II appears to provide the most suitable intervention window for preventive rail grinding, whereas Dynamic Phase III requires corrective maintenance due to persistent resonance excitation and severe wheel–rail noise. To support maintenance evaluation, a resonance-based effectiveness criterion was also proposed using the duration of Dynamic Phase II as a performance indicator. The proposed framework establishes a novel closed-loop “dynamic monitoring–preventive grinding” maintenance strategy that overcomes the limitations of conventional geometry-based approaches. The developed methodology provides practical support for predictive maintenance, rail noise mitigation, lifecycle cost reduction, and intelligent asset management in urban railway systems.
Keywords: Rail corrugation; wheel–rail resonance; dynamic monitoring; preventive rail grinding; predictive maintenance; urban rail transit.
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Citation: Xu, A., Liu, K., Wu, M., Duan, Y., Liu, S., and Zhu, J. (2026). Wheel–Rail Resonance Dynamic Monitoring and Preventive Grinding. Journal of Engineering, Project, and Production Management, 16(6), 2026-0113.
