Bearings are critical components in rotating machinery, making fault detection essential for operational safety and reliability. However, weak bearing faults are difficult to detect due to strong noise interference and the masking effect of background vibrations. To address these challenges, this paper proposes a novel weak fault detection method by combining optimized successive jump mode decomposition (OSJMD) with a blind deconvolution technique based on generalized Gaussian cyclostationarity (CYCBDβ). CYCBDβ offers superior robustness against both Gaussian and non-Gaussian noise compared to existing methods. SJMD effectively separates noise from raw signals and extracts impulse-like features. To enhance SJMD’s performance, a synergistic swarm optimization (SSO) algorithm is used to optimize the key parameter—mode compactness α. By integrating OSJMD and CYCBDβ, the proposed method significantly improves the detection of weak fault signals. Simulation and experimental results demonstrate its effectiveness and superiority over conventional approaches.
AbualigahLHusseinAMAAlmomaniMH, et al. (2024) Improved synergistic swarm optimization algorithm to optimize task scheduling problems in cloud computing. Sustainable Computing: Informatics and Systems43: 101012.
2.
AthisayamAKondalM (2024) A smart CEEMDAN, bessel transform and CNN-based scheme for compound gear-bearing fault diagnosis. Journal of Vibration Engineering and Technologies12: 393–412.
3.
BuzzoniMAntoniJD’EliaG (2018) Blind deconvolution based on cyclostationarity maximization and its application to fault identification. Journal of Sound and Vibration432: 569–601.
4.
ChenWHWeiCHLuXM (2015) Stochastic finite-time stabilization for a class of nonlinear Markovian jump stochastic systems with impulsive effects. Journal of Dynamic Systems Measurement and Control-Transactions of the ASME137(4): 044501.
5.
ChenBSongDZhangW, et al. (2021a) A performance enhanced time-varying morphological filtering method for bearing fault diagnosis. Measurement: Journal of the International Measurement Confederation176: 109163.
6.
ChenGYanCMengJ, et al. (2021b) Improved VMD-FRFT based on initial center frequency for early fault diagnosis of rolling element bearing. Measurement Science and Technology32(11): 115024.
7.
ChenYMaoZHouX, et al. (2024) Noise-robust adaptive feature mode decomposition method for accurate feature extraction in rotating machinery fault diagnosis. Mechanical Systems and Signal Processing211: 111213.
8.
DragomiretskiyKZossoD (2014) Variational mode decomposition. IEEE Transactions on Signal Processing62(3): 531–544.
9.
DuHWangJQianW, et al. (2024) Rotating machinery fault diagnosis based on parameter-optimized variational mode decomposition. Digital Signal Processing: A Review Journal153: 104590.
10.
FangBHuJYangC, et al. (2023) Minimum noise amplitude deconvolution and its application in repetitive impact detection. Structural Health Monitoring22(3): 1807–1827.
11.
GuRChenJHongR, et al. (2020) Incipient fault diagnosis of rolling bearings based on adaptive variational mode decomposition and Teager energy operator. Measurement: Journal of the International Measurement Confederation149: 106941.
12.
HanYYangNWeiY, et al. (2025) Extraction of mechanical multi-fault using parameter-adaptive jump plus mode decomposition. Signal, Image and Video Processing19(10): 821.
13.
HeLYiCWangD, et al. (2021) Optimized minimum generalized Lp/Lq deconvolution for recovering repetitive impacts from a vibration mixture. Measurement: Journal of the International Measurement Confederation168: 108329.
14.
HeCChengXXiongL, et al. (2025) A novel maximum impulse amplitude deconvolution for rolling bearing weak fault identification enhanced by spectrum information guided VMD. Mechanical Systems and Signal Processing237: 112973.
15.
HeidariAAMirjaliliSFarisH, et al. (2019) Harris hawks optimization: algorithm and applications. Future Generation Computer Systems97: 849–872.
16.
HuangJWangJYangJ, et al. (2025) Oscillation search robust dynamic mode decomposition method and its application in rolling bearing fault diagnosis. Measurement Science and Technology36: 16138.
17.
JosephTSudeepUKeerthi KrishnanK (2025) CycleGAN denoising of recurrence plots of rolling bearing vibration signals for improved fault classification. Journal of Mechanical Science and Technology39(2): 497–508.
18.
LiYChengX (2025) Double-optimized symmetric geometric mode decomposition with dispersion entropy and its application in feature extraction. Signal Processing235: 110046.
19.
LiJZhengJPanH, et al. (2023) Two-dimensional composite multi-scale time–frequency reverse dispersion entropy-based fault diagnosis for rolling bearing. Nonlinear Dynamics111(8): 7525–7546.
20.
LiFWangCLiuZ, et al. (2024) Sparrow search algorithm-optimized variational mode decomposition-based multiscale convolutional network for cavitation diagnosis of hydro turbines. Ocean Engineering312: 119055.
21.
LiSYanCHouY, et al. (2025) Compound fault diagnosis method for rolling bearings based on enhanced MED and adaptive periodized symplectic geometry mode decomposition. Structural Health Monitoring. Epub ahead of print 2025. DOI: 10.1177/14759217251314703.
22.
McdonaldGLZhaoQ (2017) Multipoint optimal minimum entropy deconvolution and convolution fix: application to vibration fault detection. Mechanical Systems and Signal Processing82: 461–477.
23.
McDonaldGLZhaoQZuoMJ (2012) Maximum correlated kurtosis deconvolution and application on gear tooth chip fault detection. Mechanical Systems and Signal Processing33: 237–255.
24.
MiaoYZhaoMMakisV, et al. (2019) Optimal swarm decomposition with whale optimization algorithm for weak feature extraction from multicomponent modulation signal. Mechanical Systems and Signal Processing122: 673–691.
25.
MiaoYWangJZhangB, et al. (2022) Practical framework of gini index in the application of machinery fault feature extraction. Mechanical Systems and Signal Processing165: 108333.
26.
NazariMSakhaeiSM (2020) Successive variational mode decomposition. Signal Processing174: 107610.
27.
NazariMKorshøjARRehmanN (2025) Successive jump and mode decomposition. Epub ahead of print 2025.
28.
PangBWangBHuY, et al. (2022) Clustering group-sparse mode decomposition and its application in rolling bearing fault diagnosis. IEEE Transactions on Instrumentation and Measurement71: 398–416.
29.
PengDZhuXTengW, et al. (2023) Use of generalized Gaussian cyclostationarity for blind deconvolution and its application to bearing fault diagnosis under non-Gaussian conditions. Mechanical Systems and Signal Processing196: 110351.
30.
SawalhiNRandallRBEndoH (2007) The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis. Mechanical Systems and Signal Processing21(6): 2616–2633.
31.
WangBLeiYNaipengL, et al. (2018) A hybrid prognostics approach for estimating remaining useful life of rolling element bearings. IEEE Transactions on Reliability69: 401–412.
32.
WangZYLiXHGaoLJ (2022) Stochastic input-to-state stability for nonlinear impulsive switched systems with random impulses coupled by multiple jump maps. Proceedings of the Institution of Mechanical Engineers Part I-Journal of Systems and Control Engineering236(8): 1553–1565.
33.
XuYWeiYWangY, et al. (2025a) A fast iterative shrinkage and threshold deconvolution technique with application to bearing fault signals. Journal of Vibration and Control31(23–24): 4735–4747.
34.
XuYWuZWangX, et al. (2025b) Parameter-adaptive fast iterative filtering decomposition for fault diagnosis of rolling bearings in spinning frames. Measurement Science and Technology36(6): 066137.
35.
YeXHuYShenJ, et al. (2021) An adaptive optimized TVF-EMD based on a sparsity-impact measure index for bearing incipient fault diagnosis. IEEE Transactions on Instrumentation and Measurement70: 3507311.
36.
YiCZhangFHuangT, et al. (2024) Wind turbine gearbox fault diagnosis via adaptive IMFogram. Structural Health Monitoring23(4): 2542–2558.
37.
YuZTangBChenW, et al. (2024) A method based on VMD improved by SSA for leak location of water distribution. Digital Signal Processing. A Review Journal145: 104334.
38.
ZhangGWangYLiX, et al. (2022) Enhanced symplectic geometry mode decomposition and its application to rotating machinery fault diagnosis under variable speed conditions. Mechanical Systems and Signal Processing170: 108841.
