To address the issues of rolling bearing fault vibration signals being susceptible to noise interference and the support vector machine (SVM) relying on manual parameter settings, this paper proposes a fault diagnosis method based on ICEEMDAN and AIHO−SVM. Firstly, the Hippopotamus Optimization Algorithm is improved by incorporating Chebyshev chaotic mapping, refraction opposite learning, dynamic weighting, adaptive step size, and guided learning strategies, thereby enhancing convergence accuracy and speed. Secondly, ICEEMDAN is employed to decompose vibration signals for noise reduction, and the effective intrinsic mode function (IMF) components are selected according to the mutual information criterion to reconstruct the signal. Nine time−domain statistical features are then extracted to construct the fault feature vector. Thirdly, AIHO is used to collaboratively optimize the penalty factor and kernel parameters of SVM, establishing an AIHO−SVM classification model. Finally, the proposed method is validated on bearing datasets from Case Western Reserve University and Huazhong University of Science and Technology. Experimental results show that the average diagnostic accuracies on the two datasets reach 99.07% and 99.09%, respectively, demonstrating the effectiveness of the proposed method for rolling bearing fault diagnosis.
To overcome the nonlinear and non-stationary characteristics of rolling bearing vibration signals and the challenge of extracting incipient weak fault features, this paper proposes a joint fault diagnosis method based on Variational Mode Decomposition (VMD), Maximum Correlated Kurtosis Deconvolution (MCKD) and Support Vector Machine (SVM). Different from most existing studies that separately optimize individual stages of the fault diagnosis workflow, the proposed method adopts a multi-strategy enhanced grey wolf algorithm (S-LE-EGWO) to collaboratively tune parameters for multiple key modules within a unified framework. Firstly, taking the minimum envelope entropy as the fitness function, the S-LE-EGWO algorithm is utilized to optimize the mode number K and penalty factor α of VMD to realize adaptive decomposition of vibration signals. Secondly, kurtosis combined with the correlation coefficient is adopted to select effective. Intrinsic Mode Function (IMF), and the signal is reconstructed based on the screened components. Then, the S-LE-EGWO algorithm is employed to optimize the parameters of MCKD to realize effective extraction of periodic fault impulses. Finally, multi-dimensional fault features are extracted, dimension-reduced by Kernel Principal Component Analysis (KPCA), and fed into the optimized SVM classifier to complete fault identification. Feature-oriented mechanism analysis is carried out using simulation signals, and the proposed method is validated on the CWRU rolling-bearing dataset, with comparative investigations against four mainstream optimization-based diagnostic algorithms. The test results show that the proposed method can effectively mine weak fault features of bearings. 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.
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.
A novel fault diagnosis method based on adaptive signal decomposition and intelligent classification integration is proposed, outperforming other comparative models and exhibiting superior diagnostic precision and robustness.
Accurate diagnosis of rolling bearing faults is critical to the reliability of industrial equipment. However, rolling bearings often operate under complex operating conditions, and with data imbalances and noise interference, fault diagnosis of them remains extremely challenging. To address these issues, a novel mode entropy knowledge machine (MEKM) framework for robust bearing fault diagnosis is proposed in this study. For MEKM, the mode entropy space is firstly constructed to decompose the vibration signal into intrinsic mode components, and the noise-resistant feature extraction and dimensionality reduction are realized by principal component analysis. Secondly, a fast classifier based on extreme learning machines is introduced, and its parameters are automatically adjusted through a particle swarm optimization to establish an adaptive extreme learning machine diagnosis model, ensuring optimal generalization under different load and speed levels. Then, a collaborative optimization paradigm is developed to coordinate mode entropy features and classifier parameters through fully automated learning, in which entropy-driven feature characterization guides the iterative refinement of decision boundaries, while classifier feedback dynamically improves the selectivity of entropy features. Finally, validation is performed on bearings with multiple operating conditions, and the results indicated that the MEKM outperformed conventional deep learning methods in terms of diagnostic accuracy and generalization ability. The work provides a theoretical basis and an industrially feasible solution for health monitoring of mechanical equipment.
Hongchuang Tan, Yiheng Su, Jiang Ding et al.· Journal of Dynamics Monitori...· 0 citations
A method based on refined composite multi-scale attention entropy (RCMAE) and Beluga whale optimization (BWO) for multi-classification support vector machines (SVM) to solve problems of poor feature discriminability and blind parameter selection in traditional fault diagnosis is proposed.
Bing Wang, Huimin Li, Xiong Hu· Journal of Vibration and Con...· 0 citations
Experiments indicate that the proposed fault diagnosis approach that combines Continuous Wavelet Transform, Convolutional Neural Network, CNN, Black-winged Kite Algorithm, and Least Squares Support Vector Machine outperforms CNN, CNN-SVM, and CNN-BiGRU under small-sample conditions.
Shi-Yan Sun, Yujun Shi, Quan Li et al.· Italian National Conference...· 0 citations
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