Advancing high-speed train gearbox durability: enhanced bearing load and contact stress through transition from helical to herringbone gears

Hao Wu, Jing Wei, Pingbo Wu, Fansong Li, Yayun Qi

Railway Engineering Science ›› 2024, Vol. 32 ›› Issue (4) : 461-479.

Railway Engineering Science ›› 2024, Vol. 32 ›› Issue (4) : 461-479. DOI: 10.1007/s40534-024-00345-5
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Advancing high-speed train gearbox durability: enhanced bearing load and contact stress through transition from helical to herringbone gears

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Abstract

High-speed trains typically utilize helical gear transmissions, which significantly impact the bearing load capacity and fatigue service performance of the gearbox bearings. This paper focuses on the gearbox bearings, establishing dynamic models for both helical gear and herringbone gear transmissions in high-speed trains. The modeling particularly emphasizes the precision of the bearings at the gearbox’s pinion and gear wheels. Using this model, a comparative analysis is conducted on the bearing loads and contact stresses of the gearbox bearings under uniform-speed operation between the two gear transmissions. The findings reveal that the helical gear transmission generates axial forces leading to severe load imbalance on the bearings at both sides of the large gear, and this imbalance intensifies with the increase in train speed. Consequently, this results in a significant increase in contact stress on the bearings on one side. The adoption of herringbone gear transmission effectively suppresses axial forces, resolving the load imbalance issue and substantially reducing the contact stress on the originally biased side of the bearings. The study demonstrates that employing herringbone gear transmission can significantly enhance the service performance of high-speed train gearbox bearings, thereby extending their service life.

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Hao Wu, Jing Wei, Pingbo Wu, Fansong Li, Yayun Qi. Advancing high-speed train gearbox durability: enhanced bearing load and contact stress through transition from helical to herringbone gears. Railway Engineering Science, 2024, 32(4): 461‒479 https://doi.org/10.1007/s40534-024-00345-5

References

[1.]
ChenZ, ZhaiW, WangK. Vibration feature evolution of locomotive with tooth root crack propagation of gear transmission system. Mech Syst Signal Process, 2019, 115: 29-44
CrossRef Google scholar
[2.]
ZhangT, ChenZ, ZhaiW, et al. . (2019) Establishment and validation of a locomotive–track coupled spatial dynamics model considering dynamic effect of gear transmissions. Mech Syst Signal Process, 2019, 119: 328-345
CrossRef Google scholar
[3.]
NataliC, BattarraM, DalpiazG, et al. . A critical review on FE-based methods for mesh stiffness estimation in spur gears. Mech Mach Theory, 2021, 161
CrossRef Google scholar
[4.]
LuoH, BoL, PengC, et al. . Fault diagnosis for high-speed train axle-box bearing using simplified shallow information fusion convolutional neural network. Sensors, 2020, 20(17): 4930
CrossRef Google scholar
[5.]
HouD, QiH, LiD, et al. . High-speed train wheel set bearing fault diagnosis and prognostics: research on acoustic emission detection mechanism. Mech Syst Signal Process, 2022, 179
CrossRef Google scholar
[6.]
ChenS, MengY, TangH, et al. . Robust deep learning-based diagnosis of mixed faults in rotating machinery. IEEE/ASME Trans Mechatron, 2020, 25(5): 2167-2176
CrossRef Google scholar
[7.]
DolencB, BoˇskoskiP, JuričićD. Distributed bearing fault diagnosis based on vibration analysis. Mech Syst Signal Process, 2016, 66–67: 521-532
CrossRef Google scholar
[8.]
SohaibM, KimCH, KimJM. A hybrid feature model and deep-learning-based bearing fault diagnosis. Sensors, 2017, 17(12): 2876
CrossRef Google scholar
[9.]
NembhardAD, SinhaJK, PinkertonA, et al. . Fault diagnosis of rotating machines using vibration and bearing temperature measurements. Diagnostyka, 2013, 14(3): 45-51
[10.]
AmarnathM, SugumaranV, KumarH. Exploiting sound signals for fault diagnosis of bearings using decision tree. Measurement, 2013, 46(3): 1250-1256
CrossRef Google scholar
[11.]
LuS, WangX, HeQ, et al. . Fault diagnosis of motor bearing with speed fluctuation via angular resampling of transient sound signals. J Sound Vib, 2016, 385: 16-32
CrossRef Google scholar
[12.]
HouY, WangX, SunS, et al. . Measured load spectra of the bearing in high-speed train gearbox under different gear meshing conditions. Railw Eng Sci, 2023, 31(1): 37-51
CrossRef Google scholar
[13.]
HouY, WangX, QueH, et al. . Variation in contact load at the most loaded position of the outer raceway of a bearing in high-speed train gearbox. Acta Mech Sin, 2021, 37(11): 1683-1695
CrossRef Google scholar
[14.]
ShiH, WangJ, WuP, et al. . Field measurements of the evolution of wheel wear and vehicle dynamics for high-speed trains. Veh Syst Dyn, 2018, 56(8): 1187-1206
CrossRef Google scholar
[15.]
LiuY, ChenZ, WangK, et al. . Dynamic characteristics analysis of gear transmission and its support bearings of high-speed train on the curve. Veh Syst Dyn, 2023, 2(3): 623-650
CrossRef Google scholar
[16.]
LiuY, ChenZ, LiW, et al. . Dynamic analysis of traction motor in a locomotive considering surface waviness on races of a motor bearing. Railw Eng Sci, 2021, 29(4): 379-393
CrossRef Google scholar
[17.]
LiuY, ChenZ, ZhaiW, et al. . Dynamic investigation of traction motor bearing in a locomotive under excitation from track random geometry irregularity. Int J Rail Transp, 2022, 10(1): 72-94
CrossRef Google scholar
[18.]
WangZ, SongY, YinZ, et al. . Random response analysis of axle-box bearing of a high-speed train excited by crosswinds and track irregularities. IEEE Trans Veh Technol, 2019, 68(11): 10607-10617
CrossRef Google scholar
[19.]
WuH, WuP, LiF, et al. . Fatigue analysis of the gearbox housing in high-speed trains under wheel polygonization using a multibody dynamics algorithm. Eng Fail Anal, 2019, 100: 351-364
CrossRef Google scholar
[20.]
Patil S, Ambhore P (2020) Design and analysis herringbone gear use in industry. In: ICRRM 2019–system reliability, quality control, safety, maintenance and management: applications to civil, mechanical and chemical engineering, Pune, pp 53–59
[21.]
YangJ, ZhuR, LeeHP, et al. . Experimental and numerical dynamic analysis of marine herringbone planetary gearbox supported by journal bearings. J Sound Vib, 2023, 545
CrossRef Google scholar
[22.]
WangS, ZhuR. Research on dynamics and failure mechanism of herringbone planetary gearbox in wind turbine under gear surface pitting. Eng Fail Anal, 2023, 146
CrossRef Google scholar
[23.]
HouS, WeiJ, ZhangA, et al. . Study of dynamic model of helical/herringbone planetary gear system with friction excitation. J Comput Nonlinear Dyn, 2018, 13(12)
CrossRef Google scholar
[24.]
ShiH, ZengJ, GuoJ. Disturbance observer-based sliding mode control of active vertical suspension for high-speed rail vehicles. Veh Syst Dyn, 2024
CrossRef Google scholar
[25.]
WuH, WuP, GuoJ, et al. . Current signal characteristics analysis of transmission system in high-speed train under abnormal vibration conditions. Veh Syst Dyn, 2023, 61(4): 1151-1167
CrossRef Google scholar
[26.]
GargVK. Dynamics of railway vehicle systems, 2012OrlandoAcademic Press
[27.]
HouS, WeiJ, ZhangA, et al. . A novel comprehensive method for modeling and analysis of mesh stiffness of helical gear. Appl Sci, 2020, 10(19): 6695
CrossRef Google scholar
[28.]
XieC, HuaL, HanX, et al. . Analytical formulas for gear body-induced tooth deflections of spur gears considering structure coupling effect. Int J Mech Sci, 2018, 148: 174-190
CrossRef Google scholar
[29.]
LiJ, XuM, WangS. Finite element analysis of instantaneous mesh stiffness of cylindrical gears (with and without flexible gear body). Commun Numer Methods Eng, 1999, 15(8): 579-587
CrossRef Google scholar
[30.]
HedlundJ, LehtovaaraA. A parameterized numerical model for the evaluation of gear mesh stiffness variation of a helical gear pair. Proc Inst Mech Eng C J Mech Eng Sci, 2008, 222(7): 1321-1327
CrossRef Google scholar
[31.]
HarrisTA, KotzalasMN. Rolling bearing analysis—2 volume set, 2006LondonCRC Press
CrossRef Google scholar
[32.]
ZhaoH, LiangJ, LiuC. High-speed EMUs: characteristics of technological development and trends. Engineering, 2020, 6(3): 234-244
CrossRef Google scholar
Funding
National Key Research and Development Project of China(2022YFB3402901); National Natural Science Foundation of China(52302467)

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