To study the influence of random parameter on the reliability of the vehicle front stabilizer bar, the fatigue strength is analyzed according to the random fatigue strength prediction method, then the fatigue limit of the actual parts are estimated, a new fatigue failure model is improved by using the fatigue limit of actual parts. Through this model, the stress spectrum collected by real vehicle is introduced, Monte-Carlo simulation method is adopted to analyze the reliability and sensitivities of stabilizer bar under driving conditions. In addition, the influence of each basic random parameter on the failure probability is obtained. The results show that stabilizer bar diameter has a great influence on the failure probability, which provides certain reference for improving the reliability of the front stabilizer bar.
JarossiLMattesonAWoodrooffeJ.Trucks involved in fatal accidents factbook 2007. Technical Report UMTRI-2010-4, Center for National Truck and Bus Statistics, January2010. Ann Arbor, MI.
2.
KusaharaY.Feasibility study of active roll stabilizer for reducing roll angle of an experimental medium-duty truck. JSAE Rev1994; 16: 217–218.
3.
DavidJMDavidC.Active roll control of single unit heavy road vehicles. Vehicle Syst Dyn2003; 40: 229–270.
4.
StrassbergerMGuldnerJ.BMW’s dynamic drive: an active stabilizer bar system. IEEE Control Syst Mag2004; 107: 28–29.
5.
WittekAMGaskaDLazarzB, et al. Effect of shot peening on the fatigue strength of automotive tubular stabilizer bars DC 218. Arch Metall Mater2017; 61: 1963–1968.
6.
CronjéPHElsPS.Improving off-road vehicle handling using an active anti-roll bar. J Terramech2010; 47: 179–189.
7.
CeritMNartEGenelK.Investigation into effect of rubber bushing on stress distribution and fatigue behavior of anti-roll bar. Eng Fail Anal2010; 17: 1019–1027.
8.
PravinB.Design, analysis and optimization of anti-roll bar. Int J Eng Res Appl2014; 4: 137–140.
9.
XieW.An equivalent LMI representation of bounded real lemma for continuous-time systems. J Inequal Appl2008; 2008: 672905.
10.
XieW.Reliability and service logistics management for a new product. PhD Thesis, The University of Arizona, US, 2013.
11.
XieWLiaoHJinT.Maximizing system availability through joint decision on component redundancy and spares inventory. Eur J Oper Res2014; 237: 164–176.
12.
ZhangMHLiuXTWangYS, et al. Parameter distribution characteristics of material fatigue life using improved bootstrap method. Int J Damage Mech2019; 28: 772–793.
13.
WangHJLiuXTZhangMH, et al. Prediction of material fatigue parameters for low alloy forged steels considering error circle. Int J Fatigue2019; 121: 135–145.
14.
SingpurwallaND.Reliability and risk. West Sussex: Jonn Wiley & Sons Ltd., 2006.
15.
LiaoDZhuSPKeshtegarB, et al. Probabilistic framework for fatigue life assessment of notched components under size effects. Int J Mech Sci2020; 181: 105685.
16.
PetrynaYSPlannerDStangenbergF, et al. Reliability of reinforced concrete structures under fatigue. Reliab Eng Syst Saf2002; 77: 253–261.
17.
WangHYKimNH.Safety envelope for load tolerance and its application to fatigue reliability design. J Mech Des2006; 128: 919–927.
18.
WangMLLiuXTWangYS, et al. Research on assembly tolerance allocation and quality control based on fuzzy reliability. Proc Inst Mech Eng Part C: J Mech Eng Sci2016; 230: 3755–3766.
19.
ZhuSPLiuQLeiQ, et al. Probabilistic fatigue life prediction and reliability assessment of a high pressure turbine disc considering load variations. Int J Damage Mech2018; 27: 1569–1588.
20.
ZhaoYZhangY.Reliability design and sensitivity analysis of cylindrical worm pairs. Mech Mach Theory2014; 82: 218–230.
21.
LiYLZhangGBWangYQ, et al. Research on reliability allocation technology for NC machine tool meta-action. Qual Reliab Eng Int2019; 35: 2016–2044.
22.
ShenWYanRBarltropN, et al. A method of determining structural stress for fatigue strength evaluation of welded joints based on notch stress strength theory. Int J Fatigue2016; 90: 87–98.
23.
LiuBTengYLHuangQ.A novel imprecise reliability prediction method for incomplete lifetime data based on two-parameter Weibull distribution. Proc Inst Mech Eng Part O: J Risk Reliab2020; 234: 208–218.
24.
WangXLLiuXBWangYS, et al. Reliability analysis on the drive system of a gear-type oil pump with variable displacement. Adv Mech Eng2016; 8: 1–11.
25.
LiuXTZhengSLFengJZ, et al. Reliability reallocation for fuel cell vehicles based on genetic algorithm. Qual Reliab Eng Int2015; 31: 1495–1502.
26.
YuXGWangXLiuXT, et al. Reliability reallocation for cost uncertainty of fuel cell vehicles via improved differential evolution algorithm. Qual Reliab Eng Int2020; 36: 303–314.
27.
LiuXTKanFCXinXF, et al. Fatigue life prediction of clutch sleeve based on abrasion mathematical model in service period. Fatigue Fract Eng Mater Struct2020; 43: 488–501.
28.
HeJCZhuSPLiaoD, et al. Probabilistic fatigue assessment of notched components under size effect using critical distance theory. Eng Fract Mech2020; 235: 107150.
29.
LiaoDZhuSPCorreiaJAFO, et al. Recent advances on notch effects in metal fatigue: a review. Fatigue Fract Eng Mater Struct2020; 43: 637–659.
30.
MischkeCR.Prediction of stochastic endurance strength. J Vib Acoust1987; 109: 113–122.
LiuXTMaoKWangXL, et al. A modified quality loss model of service life prediction for products via wear regularity. Reliab Eng Syst Saf2020; 204: 107187.
36.
LiuXTZhangMHWangHJ, et al. Fatigue life analysis of automotive key parts based on improved peak-over-threshold method. Fatigue Fract Eng Mater Struct2020; 43: 1824–1836.
37.
WangMLLiuXTWangXL, et al. Research on load spectrum construction of automobile key parts based on Monte Carlo sampling. J Test Eval2018; 46: 1099–1110.
38.
TianW.A review of sensitivity analysis methods in building energy analysis. Renew Sustainable Energy Rev2013; 20: 411–419.
39.
MelchersREAhammedM.A fast approximate method for parameter sensitivity estimation in Monte Carlo structural reliability. Comput Struct2004; 82: 55–61.
ZhuSPLiuQPengW, et al. Computational-experimental approaches for fatigue reliability assessment of turbine blade disks. Int J Mech Sci2018; 142–143: 502–517.
42.
ZhuSPLiuQZhouJ, et al. Fatigue reliability assessment of turbine discs under multi-source uncertainties. Fatigue Fract Eng Mater Struct2018; 41:1291–1305.
