Fatigue analysis of the gearbox housing in high-speed trains under wheel polygonization using a multibody dynamics algorithm

Abstract To examine the effect of the wheel polygonization on the fatigue of the wheelset-mounted gearbox housing of a high-speed train, a three-dimensional multibody system (MBS) railway vehicle model, which accounts for the flexibilities of gearbox housing and wheelset and the nonlinear wheel-rail contact, is established to perform the finite element and multibody dynamics analysis simultaneously. Field measurements on the wheel wear, within a total running distance of 140,000 km, were conducted to state the evolution of polygonal wear with a dominant order of 20 on the wheel. Then this uneven wear on the wheel is treated as excitations in the wheel-rail interaction, followed by numerical analysis on the dynamics stress distribution on the gearbox housing under the effect of a variable amplitude of polygonal wear on the wheel. It shows that the stress distribution on the gearbox housing is significantly affected by the deformation of the wheelset. Furtherly, the Kernel density estimation (KDE) method is employed to extrapolating the dynamic stress on the gearbox to obtain the stress with a total running distance of 140,000 km, then its fatigue damage is estimated. Comparative analysis in the fatigue damage of gearbox housing with or without the wheel polygonization states that the fatigue damage with a 20th order polygonal wear is 63% larger than that without the polygonal wear on the wheel.

[1]  Huailong Shi,et al.  Flexible vibration analysis for car body of high-speed EMU , 2016 .

[2]  Peter Eberhard,et al.  Rigid-elastic modeling of meshing gear wheels in multibody systems , 2006 .

[3]  M. Hack,et al.  Stochastic Reconstruction of Loading Histories from a Rainflow Matrix , 1997 .

[4]  Fuzhong Li,et al.  Effects of polygonal wear of wheels on the dynamic performance of the gearbox housing of a high-speed train , 2018 .

[5]  Hong-Zhong Huang,et al.  Bayesian degradation assessment of CNC machine tools considering unit non-homogeneity , 2018 .

[6]  Mohamed Haddar,et al.  Effect of transmission error on the dynamic behaviour of gearbox housing , 2007 .

[7]  D. W. Scott,et al.  Multivariate Density Estimation, Theory, Practice and Visualization , 1992 .

[8]  B. Silverman Density estimation for statistics and data analysis , 1986 .

[9]  Stefano Bruni,et al.  Numerical estimation of stresses in railway axles using a train–track interaction model , 2013 .

[10]  A. K. Khosrovaneh,et al.  Fatigue loading history reconstruction based on the rainflow technique , 1990 .

[11]  Yu Sun,et al.  Effect of Differential Ballast Settlement on Dynamic Response of Vehicle–Track Coupled Systems , 2017 .

[12]  P. Johannesson Extrapolation of load histories and spectra , 2006 .

[13]  Darrell F. Socie,et al.  Modeling Variability in Service Loading Spectra , 2004 .

[14]  Fakher Chaari,et al.  Dynamic analysis of a planetary gear failure caused by tooth pitting and cracking , 2006 .

[15]  Huailong Shi,et al.  Field measurements of the evolution of wheel wear and vehicle dynamics for high-speed trains , 2018 .

[16]  Sam Efromovich,et al.  Minimax theory of nonparametric hazard rate estimation: efficiency and adaptation , 2016 .

[17]  Weigang Hu,et al.  Fatigue failure analysis of high speed train gearbox housings , 2017 .

[18]  Subhash Rakheja,et al.  Dynamic responses of a high-speed railway car due to wheel polygonalisation , 2018 .

[19]  Maoru Chi,et al.  Influence of polygonal wear of railway wheels on the wheel set axle stress , 2015 .

[20]  D. W. Scott,et al.  On Locally Adaptive Density Estimation , 1996 .

[21]  Ahmed A. Shabana,et al.  Dynamics of Multibody Systems , 2020 .

[22]  Ramon Sancibrian,et al.  Gear transmission dynamic: Effects of tooth profile deviations and support flexibility , 2014 .