A Rolling Bearing Fault Diagnosis Method Using Adaptive Decomposition and Impact Feature Enhancement Fusion

A Rolling Bearing Fault Diagnosis Method Based on S-LE-EGWO Jointly Optimizing VMD, MCKD and SVM

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.

Adaptive Weighted Feature Mode Decomposition and Its Application in Bearing Fault Diagnosis

Rolling Bearing Fault Diagnosis Under Variable Operating Conditions Using Group Sparse Reconstruction and Multi-Strategy Improved Quantum Particle Swarm Optimized RVM

To address the problems of enhanced non-stationarity, significant feature distribution shift, and insufficient cross-condition generalization capability of traditional fault diagnosis methods under variable operating conditions such as varying speed and load, a rolling bearing fault diagnosis method integrating group sparse reconstruction and a multi-strategy improved quantum particle swarm optimization-based relevance vector machine (RVM) is proposed. First, group sparse representation learning is employed to reconstruct the original vibration signals, thereby suppressing background noise and enhancing fault-related impulsive components to improve signal separability and stability. Subsequently, a modal component selection criterion combining kurtosis and correlation coefficients is introduced to optimize and reconstruct the decomposed modal components, enabling the reconstructed signals to retain more fault-sensitive information. On this basis, multiple information entropy features are extracted from the reconstructed signals to construct high-dimensional state feature vectors for comprehensively characterizing the dynamic operating states of rolling bearings. To further enhance the parameter optimization capability, Chebyshev chaotic mapping is incorporated into the quantum particle swarm optimization (QPSO) algorithm to improve the uniformity of population initialization. Meanwhile, a Cauchy mutation strategy is introduced to strengthen the global search capability and avoid premature convergence, thereby forming a multi-strategy improved QPSO algorithm. Finally, the improved optimization algorithm is utilized to adaptively optimize the key hyperparameters of the RVM, resulting in a fault diagnosis model with high accuracy, strong generalization capability, and sparse characteristics. Experimental validation on the HUST and XJTU-SY bearing datasets demonstrates that the proposed MIQPSO-RVM framework achieves diagnostic accuracies of 96.70% and 94.83%, respectively. Compared with several representative intelligent diagnosis methods and deep learning models, the proposed method exhibits superior diagnostic performance, robustness, and generalization capability under complex operating conditions.

Mode entropy knowledge machine: a fully automated bearing fault diagnosis model for complex operating conditions

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.

Noise-Robust Bearing Fault Diagnosis via Enhanced EFD and QPSOL-Optimized SVM

An optimization‑driven framework for noise‑robust bearing fault diagnostics aimed at enhancing the reliability and design performance of rotating machinery systems and provides a basis for simulation‑driven design optimization and reliability‑oriented decision‑making in mechanical systems, enabling more effective predictive maintenance strategies and lifecycle performance improvement.

A Rolling Bearing Fault Diagnosis Method Based on ICEEMDAN and AHO−SVM

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.