Interference fit is a common way for joints between bearing and shaft in rotor-bearing system. However, in practical engineering design, the actual interference fit is subject to uncertainties arising from multiple design parameters. To this end, a reliability model of bearing-shaft interference fit considering the influence of centrifugal force is proposed based on the bearing-shaft assembly model combined with the four-order moment method and the reliability sensitivity analysis of variables is conducted. The reliability of rotor-bearing system at different rotational speeds and the sensitivity of reliability to each variable is explored. The accuracy of the bearing-shaft assembly model is verified by comparing the results with the published literature, and the numerical results of the proposed method are compared with those of Monte Carlo simulation (MCS), and the feasibility of the proposed method is demonstrated. It turns out that the reliability model can better quantify the level of tightness of the joint between the bearing and shaft, thereby providing valuable theoretical insights for the structural design of rotor-bearing systems.
GaoSChengKChenS, et al. Computational design and analysis of aerostatic journal bearings with application to ultra-high speed spindles. Proc IMechE, Part C: J Mechanical Engineering Science2017; 231(7): 1205–1220.
2.
GouNChengKHuoD. Design and analysis methods for aerostatic bearings: The past, the present, and the future. Proc IMechE, Part J: J Engineering Tribology2024; 238(10): 1198–1211.
3.
WangHXiangJZhaoX, et al. A kinematic precision reliability evaluation method for rotor-bearing systems considering multi-source wear degradations and random errors. The International Journal of Advanced Manufacturing Technology2023; 124(11): 4159–4173.
4.
YaoQDaiLTangJ, et al. High-speed rolling bearing lubrication reliability analysis based on probability box model. Probabilistic Engineering Mechanics2024; 76: 103612.
5.
BarbosaMPFRadeDA. Kriging/FORM reliability analysis of rotor-bearing systems. Journal of Vibration Engineering & Technologies2022; 10(6): 2179–2201.
6.
XieBWangYZhuY, et al. Time-variant reliability analysis of angular contact ball bearing considering the coupled effect of rolling contact fatigue damage and wear. Reliability Engineering & System Safety2024; 241: 109667.
7.
ZhangYLiuY. Modeling of the rotor-bearing system and dynamic reliability analysis of rotor’s positioning precision. Proc IMechE, Part O: J Risk and Reliability2021; 235(3): 491–508.
8.
ZhangYGuJ. Dynamic reliability analysis of angular contact ball bearings in positioning precision. Proc IMechE, Part O: J Risk and Reliability2020; 234(6): 723–734.
9.
WuMHanXTaoY, et al. Lubrication reliability analysis of wind turbine main bearing in random wind field. Tribology International2023; 179: 108181.
10.
ZhangPJiangZHuangX, et al. Thermal error prediction and reliability analysis of the main shaft bearing at wind turbines. Proc IMechE, Part C: J Mechanical Engineering Science2024; 238(19): 9773–9792.
11.
JiangZHuangXWangB, et al. Time-dependent reliability-based design optimization of main shaft bearings in wind turbines involving mixed-integer variables. Reliability Engineering & System Safety2024; 243: 109817.
12.
LiuYZhangYWuZ. Stochastic dynamic analysis of the rotor–bearing system considering the randomness of the radial clearance. Journal of the Brazilian Society of Mechanical Sciences and Engineering2019; 41: 1–13.
13.
HongJLiuFMaY, et al. Composite failure analysis of an aero-engine inter-shaft bearing inner ring. Engineering Failure Analysis2024; 165: 108707.
14.
DongAWeiYKumarA, et al. Dynamic analysis of full-ceramic bearing-rotor system under thermally induced loosening in aerospace applications. Engineering Failure Analysis2024; 159: 108080.
15.
SuYShiLZhouK, et al. Knowledge-informed deep networks for robust fault diagnosis of rolling bearings. Reliability Engineering & System Safety2024; 244: 109863.
16.
LiXWangYYaoJ, et al. Multi-sensor fusion fault diagnosis method of wind turbine bearing based on adaptive convergent viewable neural networks. Reliab Eng Syst Saf2024; 245: 109980.
17.
LiangPTianJWangS, et al. Multi-source information joint transfer diagnosis for rolling bearing with unknown faults via wavelet transform and an improved domain adaptation network. Reliab Eng Syst Saf2024; 242: 109788.
18.
CuiLXiaoYLiuD, et al. Digital twin-driven graph domain adaptation neural network for remaining useful life prediction of rolling bearing. Reliab Eng Syst Saf2024; 245: 109991.
19.
ChenWLiYLiuZ, et al. Prediction model for bearing surface friction coefficient in bolted joints based on GA-BP neural network and experimental data. Tribol Int2025; 201: 110217.
20.
LiXChenYLiuY. A novel convolutional neural network with global perception for bearing fault diagnosis. Eng Appl Artif Intell2025; 143: 109986.
21.
WangLCaoHYeZ, et al. Bayesian large-kernel attention network for bearing remaining useful life prediction and uncertainty quantification. Reliab Eng Syst Saf2023; 238: 109421.
22.
DingWLiJMaoW, et al. Rolling bearing remaining useful life prediction based on dilated causal convolutional DenseNet and an exponential model. Reliab Eng Syst Saf2023; 232: 109072.
23.
ChenCLiBGuoJ, et al. Bearing life prediction method based on the improved FIDES reliability model. Reliab Eng Syst Saf2022; 227: 108746.
24.
SongLKTaoFLiXQ, et al. Physics-embedding multi-response regressor for time-variant system reliability assessment. Reliab Eng Syst Saf2025: 111262.
25.
YuAHuangHZLiH, et al. Reliability analysis of rolling bearings considering internal clearance. J Mech Sci Technol2020; 34: 3963–3971.
26.
WangZChenSHeW, et al. Research for the bearing fit effect on the vibration characteristics of spindle rotor-bearing system. J Mech Sci Technol2023; 37(12): 6257–6270.
27.
FangBZhangJ. Analytical determination of the optimal clearance for the fatigue life of ball bearing under different load conditions. J Tribol2022; 144(2): 021203.
28.
ZhangJFangBZhuY, et al. A comparative study and stiffness analysis of angular contact ball bearings under different preload mechanisms. Mech Mach Theory2017; 115: 1–17.
29.
JiangYZhuTDengS. Combined analysis of stiffness and fatigue life of deep groove ball bearings under interference fits, preloads and tilting moments. J Mech Sci Technol2023; 37(2): 539–553.
30.
WangZZhangKWangZ, et al. Research on vibration of ceramic motorized spindle influenced by interference and thermal displacement. J Mech Sci Technol2021; 35(6): 2325–2335.
31.
BarmanovISOrtikovMN. Ball bearing dynamics at the interference fit on balls. Procedia Eng2017; 176: 19–24.
32.
HanSZhangHZhuQ, et al. Nonlinear vibration characteristics of an aeroengine cantilever flexible rotor considering interference fit and unbalance. Mech Based Des Struct Mach2024: 1–28.
33.
LiuYZhangHHuP, et al. Research on the vibration characteristics of the ball bearing considering the uncertain bearing-shaft interference fit. Proc IMechE, Part C: J Mechanical Engineering Science2024; 238(1): 48–63.
34.
LiuYZhangHHuP, et al. Dynamic modeling and reliability analysis of the ball bearing rotating accuracy considering the uncertain bearing-shaft interference fit. Proc IMechE, Part C: J Mechanical Engineering Science2025; 239(2): 389–401.
35.
LiuGHongJWuW, et al. Investigation on the influence of interference fit on the static and dynamic characteristics of spindle system. Int J Adv Manuf Technol2018; 99: 1953–1966.
36.
MaHYaoHLRenZH, et al. Numerical simulation and experimental research on pedestal looseness of a rotor system. In: Int Des Eng Tech Conf Comput Inf Eng Conf2007; 4806: 895–901.
37.
MaHZhaoXTengY, et al. Analysis of dynamic characteristics for a rotor system with pedestal looseness. Shock Vib2011; 18(1–2): 13–27.
38.
WeiSLuWChuF. Speed characteristics of disk–shaft system with rotating part looseness. J Sound Vib2020; 469: 115127.
39.
LurieAI.
Theory of elasticity. Berlin: Springer Science & Business Media, 2010.
40.
ZhangYWenBLiuQ. First passage of uncertain single degree-of-freedom nonlinear oscillators. Comput Methods Appl Mech Eng1998; 165(1–4): 223–231.
41.
ZhangYMWenBCLiuQL. Reliability sensitivity for rotor–stator systems with rubbing. J Sound Vib2003; 259(5): 1095–1107.
42.
WuYT. Computational methods for efficient structural reliability and reliability sensitivity analysis. AIAA J1994; 32(8): 1717–1723.
