Fatigue data editing (FDE) is an effective scheme to shorten the time of durability bench testing of vehicle components. In this paper, four existing FDE method for vehicle components were evaluated and compared. Moreover, a novel FDE method based on variational mode decomposition (VMD) algorithm was proposed. In this method, the VMD algorithm was used to decompose the fatigue data into several intrinsic mode function (IMF) components. The instantaneous energy spectrum of the fatigue data was obtained using the Hilbert transform to analyze the IMF components. Then, the damaging segments were extracted to obtain the edited fatigue data. Fatigue data measured at powertrain mount was used for method comparative study. Results indicate that the time compression ratio of the VMD-based method is about 16.09%, which is larger than that of other editing methods. Furthermore, the experiment data shows that the editing effect of the proposed method were encouraging.
LiuZGaoYYangJ, et al. Transfer path analysis and its application to diagnosis for low-frequency transient vibration in the automotive door slamming event. Measurement2021; 183: 109896.
2.
KimSParkHMoonB, et al. The prediction methodology for tire’s high speed durability regulation test using a finite element method. Int J Fatigue2019; 118: 77–86.
3.
GehrmannOKrgerNHMuhrA.Displacement-controlled fatigue testing of rubber is not strain-controlled. Int J Fatigue2021; 145: 106083.
4.
KimJHJeongHY.A study on the material properties and fatigue life of natural rubber with different carbon blacks. Int J Fatigue2005; 27: 263–272.
5.
YuJWZhaoLHMaJJ, et al. Load spectrum compilation for vehicle road simulation test by applying a new time domain threshold editing method. Proc IMechE, Part D: J Automobile Engineering2020; 200: 73–88.
6.
ConleATopperTH.Evaluation of small cycle omission criteria for shortening of fatigue service histories. Int J Fatigue1979; 1: 23–28.
7.
ConleATopperTH.Overstrain effects during variable amplitude service history testing. Int J Fatigue1980; 2: 130–136.
8.
WangBJLiQRenZS, et al. Improving the fatigue reliability of metro vehicle bogie frame based on load spectrum. Int J Fatigue2020; 132: 105389.
9.
KobayashiTShockeyDASchmidtCG.Assessment of fatigue load spectrum from fracture surface topography. Int J Fatigue1997; 19: 237–244.
10.
StephensRIDindingerPMGungerJE.Fatigue damage editing for accelerated durability testing using strain range and SWT parameter criteria. Int J Fatigue1997; 19: 599–606.
11.
ParaforosMDimitrisSGriepentrogR, et al. Methodology for designing accelerated structural durability tests on agricultural machinery. Biosyst Eng2016; 149: 24–37.
12.
PutraTEAbdullahSSchrammD, et al. Reducing cyclic testing time for components of automotive suspension system utilising the wavelet transform and the Fuzzy C-means. Mech Syst Signal Process2017; 90: 1–14.
13.
AustenIGregoryR.Component test during duration prediction and acceleration by fatigue analysis and fatigue editing. VTT Symposium1995; 3: 16–87.
14.
ZhuMWuXJZhengGF.Load Signal Edition method based on the short-time Fourier transform to durability test of vehicle component. J Mech Eng2019; 55: 126–151. In Chinese.
15.
LiuZPengCYangX.Research and analysis of the wheeled vehicle load spectrum editing method based on short-time Fourier transform. Proc IMechE, Part D: J Automobile Engineering2019; 233: 3671–3683.
16.
WenCKXieBLiZ, et al. Power density based fatigue load spectrum editing for accelerated durability testing for tractor front axles. Biosyst Eng2020; 200: 73–88.
17.
PratumnopharatPLeungPSCourtRS. Wavelet transform-based stress-time history editing of horizontal axis wind turbine blades. Renew Energy2014; 63: 558–575.
18.
ShangguanWBZhengGFRakhejaS, et al. A method for editing multi-axis load spectrums based on the wavelet transforms. Measurement2020; 162: 107903.
19.
AbdullahSNizwanCYunohM, et al. Fatigue features extraction of road load time data using the S-transform. Int J Auto Tech-Kor2013; 14: 805–815.
20.
WangLMengX.An adaptive generalized S-transform for instantaneous frequency estimation. Signal Process2011; 91: 1876–1886.
21.
DragomiretskiyKZossoD.Variational mode decomposition. IET Signal Process2014; 62: 531–544.
22.
ZhangXMiaoQZhangH, et al. A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery. Mech Syst Signal Process2018; 108: 58–72.
23.
ZhouXLiYJiangL, et al. Fault feature extraction for rolling bearings based on parameter-adaptive variational mode decomposition and multi-point optimal minimum entropy deconvolution. Measurement2021; 173: 108469.
24.
DingMDuBWangH, et al. A signal de-noising method for a MEMS gyroscope based on improved VMD-WTD. Meas Sci Technol2021; 32: 095112.
25.
HeLAkebonoHSugetaA, et al. Cumulative fatigue damage of stress below the fatigue limit in weldment steel under block loading. Fatigue Fract Eng M2020; 43: 1419–1426.
26.
LiuXNZhaoXZShangguanWB.Fatigue life prediction of natural rubber components using an artificial neural network. Fatigue Fract Eng M2022; 1: 1–12.
27.
ChengJQianZLiZ.A Cumulative fatigue damage model of polysilicon films for MEMS resonator under repeated loadings. Int J Fatigue2021; 147: 106186.
28.
LiuXNZhaoXZLiuXA, et al. A load spectrum editing method of time-frequency for rubber isolators based on the continuous wavelet transform. Measurement2022; 198: 111374.
29.
TrappAWolfsteinerAP.Frequency-domain characterization of varying random vibration loading by a non-stationarity matrix. Int J Fatigue2020; 146: 106115.
30.
QhpaBMgCJaB, et al. Rainflow-counting matrix interpolation over different operating conditions for hydroelectric turbine fatigue assessment. Renew Energy2021; 172: 465–476.
31.
WangLPengYXieY, et al. A new iteration regularization method for dynamic load identification of stochastic structures. Mech Syst Signal Process2021; 156: 107586.
