Sep 2026· International Conference on Signal Processing and Communication Security· Vol 14374, pp. 1437402 - 1437402-5· 0 citations· 10 references
Engineering
Abstract
A novel rolling bearing fault diagnosis approach integrating IZOA-VMD and the Teager envelope spectrum is proposed to overcome the inaccuracies of manual VMD parameter tuning. An Improved Zebra Optimization Algorithm (IZOA)— incorporating Sine chaotic mapping, elite opposition-based learning, and dynamic neighborhood local search—adaptively optimizes VMD. The Teager envelope spectrum is then applied to the decomposed modes to enhance fault signatures. Experimental validation on inner race, outer race, and rolling element defects demonstrates precise fault identification. This method effectively elevates decomposition accuracy and feature extraction, proving highly viable for practical engineering.
To address the difficulty in extracting and identifying failure modes when rolling bearings fail during operation, this paper proposes a method that combines Variational Mode Decomposition (VMD) optimized by the Subtraction Average Based Optimizer (SABO) with Wavelet Threshold Denoising (WTD) to extract failure feature...
Hai-Yu Luo, Zhi-Wen Li, Hao-Hao Ma et al.· Journal of Physics, Conferen...· 0 citations
Compared with other algorithms, the presented method achieves superior identification performance and possesses favorable recognition capability for incipient weak faults, which can realize the classification of bearing faults.
Extracting weak fault-related impulses from rolling bearing vibration signals remains difficult, because the signals are non-stationary and heavily contaminated by noise, and because the parameters of variational mode decomposition (VMD) still have to be set manually. This paper proposes a method that combines GOOSE-op...
Jun Wang, Ying Peng, Zi-Heng Huang et al.· Applied Sciences· 0 citations
Aiming at the difficulties in extracting gear fault feature information and the unsatisfactory performance of commonly used machine learning classification models in terms of feature separability and stable within-distribution recognition, this paper proposes a fault diagnosis method combining variational mode decompos...
Duo-Wen Chen, Hao-Ze Lv, Xin-Ze Li et al.· Entropy· 0 citations
To address the challenge of comprehensively characterizing bearing fault features using a single sensor in complex industrial environments, a bearing fault diagnosis method integrating adaptive-pooling-based weighted multi-modal feature fusion (MMFF) with a three-dimensional convolutional neural network (3DCNN) is prop...
Zhen-Fang Fu, Chang-Xian Li, Wen-Jing Guo et al.· IEEE Signal Processing Lette...· 0 citations
Rolling bearings are among the most critical components in rotating machinery, and their reliable condition assessment is essential for preventing unexpected failures and enabling predictive maintenance strategies. In this study, a lightweight and automated methodology is proposed for bearing Fault Detection (FD) and F...
C. Cosenza, M. Spirto, Francesco Melluso et al.· Journal of Physics, Conferen...· 0 citations
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