Rolling bearing fault features are often weak and easily affected by noise and interference. To improve fault feature extraction performance, this paper proposes an AHBKA-VME-SMHD method. First, the black-winged kite algorithm is improved by opposition-based learning, a Gompertz-based adaptive step size strategy, and an NGO-inspired random displacement strategy. Then, the improved algorithm is used to optimize the penalty factor and desired mode center frequency of VME, guided by a composite fitness function combining Higuchi fractal dimension and energy concentration index. Finally, SMHD is applied to enhance periodic impulsive components, and envelope spectrum analysis is used to identify fault characteristic frequencies. The proposed method is validated using simulated signals and two real-world bearing datasets, namely the CWRU and XJTU-SY datasets. The results show that the proposed method extracts clearer fault-related harmonics than the comparison methods. In addition, it obtains higher kurtosis and Gini index values and lower envelope spectrum entropy values, demonstrating its effectiveness for rolling bearing fault feature extraction.
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.
To address the challenge where early fault signals of rolling bearings are easily submerged by strong noise, and fault features are difficult to extract under harsh working conditions, this paper proposes an adaptive fault diagnosis method termed HCF-IAPO-VME. Firstly, a novel harmonic coherence factor (HCF) is constru...
Ming Zhang, Xiao-Ling Liu, Qiang-Jun Ding et al.· Lubricants· 0 citations
This paper proposes a rolling bearing fault diagnosis approach based on vibration signal analysis. The collected vibration signals are first processed through denoising, normalization, and segmentation to improve data quality and provide reliable inputs for subsequent fault feature extraction and diagnosis. A multidoma...
Wen-Bo He· International Conference on...· 0 citations
In response to the serious noise interference in the fault signals obtained by the vibration sensors and the difficulty in effectively extracting the fault characteristics, a rolling bearing fault diagnosis method based on adaptive modal decomposition and correlation kurtosis feature enhancement is proposed. This met...
Weak fault modulation components in motor stator current signals are easily masked by dominant low-frequency components, and redundant multi-domain features can further degrade bearing fault classification. To address these problems, this paper proposes a feature selection method based on an adaptive binary grey wolf o...
Hai-Tao Liang, Guo-Fu Li· Engineering Research Express· 0 citations
A fault diagnosis method combining Ensemble Window Auto-Regressive Power Spectral Density (EWAR-PSD) and ECA-VGG16 that achieves high and stable diagnostic accuracy under complex operating conditions and exhibits strong robustness to noise.
Tian-Chi Li, Yi-Min Zhang, Shu-Zhi Gao et al.· Transactions of the Canadian...· 0 citations
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