To deal with the issue of composite fault diagnosis of automotive transmission shaft bearings under strong background noise interference, a feature extraction method of optimizing recursive variational mode extraction parameters based on spider wasp optimizer (SWO) is proposed. SWO is applied to determine the optimal values of mode extraction parameters. Effective weight kurtosis criterion is employed to reduce the background noise interference. The introduction of a two-dimensional Chebyshev-Logistic-Infinite collapse map increases the diversity and randomness of the population, shortens running time, and increases fault feature coefficient. The population is divided into multiple subgroups based on fast nondominated sorting, and an adaptive grouping strategy is adopted to accelerate the convergence speed of SWO. The results demonstrate the approach can suppress the interference noise with effect, and gain a good balance between search speed and feature extraction accuracy.
ZouLLieYJiaB, et al. Research on coupled vibrations of driveshaft-rear axle system based on nonlinear hybrid model. J Vib Control2022; 28(15–16): 1976–1991.
2.
SimJMinJKimS, et al. Construction of bearing health indicator under time-varying operating conditions based on Isolation Forest. Eng Appl Artif Intel2023; 126: 107058.
3.
SongSHuangYHuaC, et al. Transmission path and influencing factors of abnormal broadband noise and vibration of the generator induced by the driveline. J Sound Vib2023; 556: 117730.
4.
MiaoYLiCZhangB, et al. Application of a coarse-to-fine minimum entropy deconvolution method for rotating machines fault detection. Mech Syst Signal Process2023; 198: 110431.
5.
LiXWuXWangT, et al. Fault diagnosis method for imbalanced data based on adaptive diffusion models and generative adversarial networks. Eng Appl Artif Intel2025; 147: 110410.
6.
ShiHMiaoYLiC, et al. A novel bearing intelligent fault diagnosis method based on spectrum sparse deep deconvolution. Eng Appl Artif Intel2024; 133: 108102.
7.
LiXXiaoSLiQ, et al. The bearing multi-sensor fault diagnosis method based on a multi-branch parallel perception network and feature fusion strategy. Reliab Eng Syst Safe2025; 261: 111122.
8.
LiPDangJJiaR, et al. Research on wind turbine composite fault decoupling and slight fault extraction based on continuous spectral kurtosis deconvolution. J Vib Eng Technol2024; 12: 2975–2986.
9.
WangHYanCWangZ, et al. Compound fault diagnosis method for rolling bearings based on the multipoint kurtosis spectrum and AO-MOMDEA. Meas Sci Technol2023; 34(9): 095012.
10.
ZhouHZhuLZhongF, et al. Composite fault feature extraction of rolling bearing using adaptive circulant singular spectrum analysis. Meas Sci Technol2023; 34(12): 125031.
11.
KumarAZhouYXiangJ.Optimization of VMD using kernel-based mutual information for the extraction of weak features to detect bearing defects. Measurement2021; 168: 108402.
12.
LiHWuXLiuT, et al. Composite fault diagnosis for rolling bearing based on parameter-optimized VMD. Measurement2022; 201: 111637.
13.
ZhouJWangJ.A novel underdetermined source number estimation for coupled vibration sources of mechanical fault based on variational mode decomposition. J Mech Sci Technol2022; 36(2): 621–635.
14.
MengZLiuJLiuJ, et al. An improved variational mode decomposition method based on spectrum reconstruction and segmentation and its application in rolling bearing fault diagnosis. Digit Signal Process2023; 141: 104161.
15.
ZhouJWuSLiuT, et al. Application of IPSO-MCKD-IVMD-CAF in the compound fault diagnosis of rolling bearing. Meas Sci Technol2023; 34(3): 35113.
16.
YangJBaiYChengY, et al. A new model for bearing fault diagnosis based on optimized variational mode decomposition correlation coefficient weight threshold denoising and entropy feature fusion. Nonlinear Dynam2023; 111(18): 17337–17367.
17.
ZhangWLiJLiT, et al. Research on feature ex-traction and separation of mechanical multiple faults based on adaptive variational mode decomposition and comprehensive impact coefficient. Meas Sci Technol2023; 34(2): 25110.
18.
YuanJSongZJiangH, et al. The Msegram: a useful multichannel feature synchronous extraction tool for detecting rolling bearing faults. Mech Syst Signal Process2023; 187: 109923.
19.
HuangXXieTHuJ, et al. Three-dimensional hybrid fusion networks for current-based bearing fault diagnosis. Meas Sci Technol2024; 35(2): 25126.
20.
LiCLiuYLiaoY, et al. A VME method based on the convergent tendency of VMD and its application in multi-fault diagnosis of rolling bearings. Measurement2022; 198: 111360.
21.
LiCLiuYLiuZ, et al. A new fault diagnosis method for wheelset-bearing system based on VME convergence tendency diagram. Meas Sci Technol2023; 34(8): 085107.
22.
PangBNazariMTangG.Recursive variational mode extraction and its application in rolling bearing fault diagnosis. Mech Syst Signal Process2022; 165: 108321.
23.
ShiPLiMZhangW, et al. Weak signal enhancement for machinery fault diagnosis based on a novel adaptive multi-parameter unsaturated stochastic resonance. Appl Acoust2022; 189: 108609.
24.
ZhengKTangJShiY, et al. A maximum cyclic impulses energy ratio deconvolution fused with enhanced envelope derivative operator frequency spectrum for early fault diagnosis of rolling bearing. Meas Sci Technol2024; 35(2): 025021.
25.
DengWLiZLiX, et al. Compound fault diagnosis using optimized MCKD and sparse representation for rolling bearings. IEEE Trans Instrum Meas2022; 71: 3508509.
26.
AthisayamAKondalM.Fault feature selection for the identification of compound gear-bearing faults using firefly algorithm. Int J Adv Manuf Tech2023; 125(3–4): 1777–1788.
27.
ZhangJDaiYXiaoY.A model for remaining useful life prediction of rolling bearings based on the IBA-FELM algorithm. Meas Sci Technol2024; 35(3): 035021.
28.
GaoSShiSZhangY.Rolling bearing compound fault diagnosis based on parameter optimization MCKD and convolutional neural network. IEEE Tran Instrum Meas2023; 71: 3508108.
29.
ZhaoHZhangPZhangR, et al. A novel performance trend prediction approach using ENBLS with GWO. Meas Sci Technol2023; 34(2): 025018.
30.
LuoPYinZZhangY, et al. A novel whale optimization algorithm based on music theory knowledge for RUL prediction of motor bearing. IEEE Trans Instrum Meas2023; 72: 3534211.
31.
FengKYangRWeiZ.An optimized Laplacian of Gaussian filter using improved sparrow search algorithm for bearing fault extraction. Meas Sci Technol2024; 35(3): 036105.
32.
YuJZhaoKGuoZ, et al. RUL prediction of rolling bearings based on adaptive variational mode decomposition and double-discriminator conditional CycleGAN. Proc IMechE, Part C: J Mechanical Engineering Science2024; 238(12): 6007–6017.
33.
LiJYaoXWangH, et al. Periodic impulses extraction based on improved adaptive VMD and sparse code shrink-age denoising and its application in rotating machinery fault diagnosis. Mech Syst Signal Process2019; 126: 568–589.
34.
AbdelBassetMMohamedRJameelM, et al. Spider wasp optimizer: a novel meta-heuristic optimization algorithm. Artif Intell Rev2023; 56: 11675–11738.
35.
JiangXJiangGWangQ, et al. Image encryption algorithm based on 2D-CLICM chaotic system. IET Image Process2023; 17(7): 2127–2141.
36.
HanQFengSWuX, et al. Two-scale topology optimization method combined with fast non-dominated sorting genetic algorithm II. Meccanica2023; 58(7): 1249–1265.
37.
WangJZhanCLiS, et al. Adaptive variational mode decomposition based on archimedes optimization algorithm and its application to bearing fault diagnosis. Measurement2022; 191: 110798.
