This paper presents a dynamic model of bearings that simulates the vibration of rail wheel bearings due to local damages such as pits on the bearing outer race. The model, based on the multi-body simulation software RecurDyn, integrates the major parts of the functionalities according to the three-dimensional model and railway axle dynamic system. Three types of surface damages on the raceway of cylindrical roller bearings were designed and evaluated by the dynamic model developed in this study. The results show that the dynamic model can effectively simulate the influence of the damages on the bearing vibration under varied pit shape and size. Furthermore, the model can predict the level of bearing damage based on the peak value of bearing vibration in the frequency domain.
Kilian MR. Rolling bearings in high speed passenger traffic. In: 2010 joint rail conference, Urbana, IL, USA, 27–29 April 2010, paper no. JRC2010-36016, pp.47–56. New York: ASME.
2.
GoharR. Elastohydrodynamics, 2nd ed. London: World Scientific, 2001, pp. 404–406.
3.
GoharRRahnejatH. Fundamentals of tribology, 2nd ed. London: Imperial College Press, 2012, pp. 147–147.
Johns-Rahnejat PM and Gohar R. Point contact elastohydrodynamic pressure distribution and sub-surface stress field, In: Tri-annual conference on multi-body dynamics: monitoring and simulation techniques, Bradford, UK, 1997.
6.
AminiAEntezamiMPapaeliasM. Onboard detection of railway axle bearing defects using envelope analysis of high frequency acoustic emission signals. Nondestruct Test Eval2016; 6: 8–16.
7.
PapaeliasMAminiAHuangZet al.Online condition monitoring of rolling stock wheels and axle bearings. Proc IMechE, Part F: J Rail Rapid Transit2016; 230: 709–723.
8.
ValeCBonifácioCSeabraJet al.Novel efficient technologies in Europe for axle bearing condition monitoring–the MAXBE project. Transp Res Procedia2016; 14: 635–644.
9.
PatelUANaikBS. Nonlinear vibration prediction of cylindrical roller bearing rotor system modeling for localized defect at inner race with finite element approach. Proc IMechE, Part K: J Multi-body Dynamics2016; 231: 647–657.
10.
WangFJingMYiJet al.Dynamic modelling for vibration analysis of a cylindrical roller bearing due to localized defects on raceways. Proc IMechE, Part K: J Multi-body Dynamics2015; 229: 39–64.
11.
SalvadorPNaranjoVInsaRet al.Axlebox accelerations: Their acquisition and time–frequency characterisation for railway track monitoring purposes. Measurement2016; 82: 301–312.
12.
ElashaFGreavesMMbaDet al.Application of acoustic emission in diagnostic of bearing faults within a helicopter gearbox. Procedia CIRP2015; 38: 30–36.
13.
VafaeiSRahnejatHAiniR. Vibration monitoring of high speed spindles using spectral analysis techniques. Int J Mach Tool Manuf2002; 42: 1223–1234.
14.
LynaghNRahnejatHEbrahimiMet al.Bearing induced vibration in precision high speed routing spindles. Int J Mach Tool Manuf2000; 40: 561–577.
15.
LiuJShaoYLimTC. Vibration analysis of ball bearings with a localized defect applying piecewise response function. Mech Mach Theory2012; 56: 156–169.
16.
PatilMSMathewJRajendrakumarPKet al.A theoretical model to predict the effect of localized defect on vibrations associated with ball bearing. Int J Mech Sci2010; 52: 1193–1201.
17.
GraneyBPStarryK. Rolling element bearing analysis. Mater Eval2012; 70: 78–85.
18.
PatelVNTandonNPandeyRK. Vibration studies of dynamically loaded deep groove ball bearings in presence of local defects on races. Procedia Eng2013; 64: 1582–1591.
19.
KıralZKaragülleH. Vibration analysis of rolling element bearings with various defects under the action of an unbalanced force. Mech Syst Signal Process2006; 20: 1967–1991.
20.
CaoHFanFZhouKet al.Wheel-bearing fault diagnosis of trains using empirical wavelet transform. Measurement2016; 82: 439–449.
21.
WangCJiaLMLiXF. Fault diagnosis method for the train axle box bearing based on KPCA and GA-SVM. Appl Mech Mater2014; 441: 376–379.
22.
YiCLinJZhangWet al.Faults diagnostics of railway axle bearings based on IMF’s confidence index algorithm for ensemble EMD. Sensors2015; 15: 10991–11011.
23.
WangXZiYHeZ. Multiwavelet denoising with improved neighboring coefficients for application on rolling bearing fault diagnosis. Mech Syst Signal Process2011; 25: 285–304.
24.
MingYChenJDongG. Weak fault feature extraction of rolling bearing based on cyclic Wiener filter and envelope spectrum. Mech Syst Signal Process2011; 25: 1773–1785.
25.
ZhangYZuoHBaiF. Classification of fault location and performance degradation of a roller bearing. Measurement2013; 46: 1178–1189.
26.
HarshaSKankarP. Stability analysis of a rotor bearing system due to surface waviness and number of balls. Int J Mech Sci2004; 46: 1057–1081.
27.
StackeLEFritzsonD. Dynamic behaviour of rolling bearings: Simulations and experiments. ARCH Proc IMechE, Part J: J Engineering Tribology2001; 215: 499–508.
28.
GuptaPK. Current status of and future innovations in rolling bearing modeling. Tribol Trans2011; 54: 394–403.
29.
StackeLEFritzsonDNordlingP. BEAST—a rolling bearing simulation tool. Proc IMechE, Part K: J Multi-body Dynamics1999; 213: 63–71.
30.
HoupertL. CAGEDYN: A contribution to roller bearing dynamic calculations, part 1: Basic tribology concepts. Tribol Trans2009; 53: 1–9.
31.
GummerASauerB. Modeling planar slider-crank mechanisms with clearance joints in RecurDyn. Multibody Syst Dyn2014; 31: 127–145.
32.
SopanenJMikkolaA. Dynamic model of a deep groove ball bearing including localized and distributed defects, part 2: Implementation and results. Proc IMechE, Part K: J Multi-body Dynamics2003; 217: 213–223.
33.
RafsanjaniAAbbasionSFarshidianfarAet al.Nonlinear dynamic modeling of surface defects in rolling element bearing systems. J Sound Vib2009; 319: 1150–1174.
34.
SawalhiNRandallRB. Vibration response of spalled rolling element bearings: Observations, simulations and signal processing techniques to track the spall size. Mech Syst Signal Process2011; 25: 846–870.
35.
Al-GhamdAMMbaD. A comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect size. Mech Syst Signal Process2006; 20: 1537–1571.
36.
Corni I, Symonds N, Wood RJK, et al. Real-time on-board condition monitoring of train axle bearings. In: The Stephenson conference - research for railways, 21–23 April 2015. London: IMechE.
37.
JiaoXJZhangJWPengBB. RecurDyn simulation of multi-body dynamic system, Beijing: Tsinghua University Press, 2010, pp. 179–180.
38.
WangYSDengSEYangBYet al.Investigation on a new five-parameter rheological model of lubricating oil. Tribology2007; 27: 461–466.
39.
He C, Xu H and Zhang Y. Analysis of the nonlinear dynamic response of gyroscope rotor system considered elasto-hydrodynamic lubrication. In: International conference on mechatronics, robotics and automation, Shenzhen, China, 14–15 May 2015.
40.
DareingDWJohnsonKL. Fluid film damping of rolling contact vibrations. ARCH J Mech Eng Sci1975; 17: 214–218.
41.
MehdigoliHRahnejatHGoharR. Vibration response of wavy surfaced disc in elastohydrodynamic rolling contact. Wear1990; 139: 1–15.
42.
KumarBRRanganathaS. A study of stiffness and damping characteristics of conventional fluid and smart fluid applied to squeeze film damper. Int J Innov Technol Exploring Eng2013; 3: 30–34.
43.
SymondsNCorniIWoodRet al.Observing early stage rail axle bearing damage. Eng Fail Anal2015; 56: 216–232.
