2026· E3S Web of Conferences· 0 citations· 7 references
TL;DR
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.
Abstract
This paper presents an optimization‑driven framework for noise‑robust bearing fault diagnostics aimed at enhancing the reliability and design performance of rotating machinery systems. The proposed approach integrates an enhanced empirical ensemble Fourier decomposition (EEFD) with a support vector machine (SVM) whose hyperparameters are optimized using a quadratic interpolation particle swarm optimization with local search (QPSOL) algorithm.
To address signal degradation under harsh industrial environments, EEFD is employed to decompose vibration signals and extract high‑quality intrinsic components. A compact yet discriminative multi‑domain feature set, including Root Mean Square (RMS), Kurtosis, and Hjorth Mobility (HM), is constructed to characterize the dynamic behaviour of the system. The QPSOL algorithm is then utilized to optimize the SVM parameters, improving convergence accuracy and classification robustness.
Experimental validation under a 10 dB signal‑to‑noise ratio demonstrates that the proposed method achieves superior diagnostic accuracy and convergence performance compared with conventional optimization techniques. Beyond fault classification, the developed framework 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.
To address the issues of parameter dependency on empirical settings, insufficient fault feature extraction capability, and limited classification accuracy in rolling bearing fault diagnosis using Variational Mode Decomposition (VMD), a novel fault diagnosis method based on adaptive signal decomposition and intelligent classification integration is proposed. The VMD parameters are adaptively optimized using the Subtraction-Average-Based Optimizer (SABO), and a kurtosis–correlation criterion is introduced to select a single fault-sensitive intrinsic mode function, from which time-domain features are extracted to construct fault feature vectors. The Moth-Flame Optimization Algorithm (MFOA) is employed to optimize the parameters of the Kernel Extreme Learning Machine (KELM) for fault state identification. From the perspective of methodological symmetry, the averaged population update of SABO is invariant to the ordering of search agents, VMD exhibits equivalence under permutation of mode labels, and KELM constructs the sample similarity matrix using a symmetric kernel function. These symmetry-related structures are integrated into the parameter optimization, modal decomposition, and fault classification stages of the proposed method. Experimental validation using the CWRU rolling bearing dataset demonstrates that the proposed method reaches a fault recognition accuracy of 96.73%, outperforming other comparative models and exhibiting superior diagnostic precision and robustness.
This paper presents a systematic framework for real-time fault-type identification in rolling element bearings using vibration signal analysis. The Case Western Reserve University bearing dataset is employed as the primary data source, comprising vibration signals recorded under different load conditions (0–3 HP). Initially, Envelope Analysis (EA) is applied to extract fault-related characteristic frequencies. While computationally efficient, EA successfully identifies fault features in 66.67% of the signals but shows limitations under noisy and spectrally smeared conditions. To address this, Cepstrum Prewhitening Analysis (CPA) is selectively applied to unresolved signals, achieving a 75% detection success in these cases and improving the overall detection rate to 91.67%. Thereafter, 36 time-domain, frequency-domain, and EA-CPA based features were extracted from segmented vibration signals. A sequential feature optimization strategy comprising variance threshold filtering, correlation analysis, Z-score normalization, ANOVA F-test feature ranking, and Recursive Feature Elimination reduced the feature set to the 10 most discriminative features. To eliminate sample- and group-level information leakage, a leakage-free Nested GroupKFold framework was developed, in which preprocessing, feature selection, and hyperparameter optimization was performed exclusively within the training folds using GridSearchCV. Six machine learning classifiers, namely Random Forest, XGBoost, LightGBM, Support Vector Machine, K-Nearest Neighbors, and Logistic Regression, were comparatively evaluated. XGBoost achieved the highest mean classification accuracy of 97.62%, while RF attained a comparable accuracy of 97.51% with lower fold-to-fold variation, indicating superior robustness and stability. Consequently, RF was selected for independent cross-condition validation, in which it was trained solely on the 0 HP operating condition and evaluated on the unseen 1 HP, 2 HP, and 3 HP datasets, demonstrating strong generalization across varying load conditions. Feature importance analysis further confirmed that the characteristic bearing defect frequencies (BPFO, BPFI, and BSF) are the dominant contributors to classification performance. The proposed framework provides an effective balance between diagnostic accuracy, robustness, interpretability, and computational efficiency, making it a promising solution for intelligent condition monitoring and predictive maintenance of rotating machinery.
Rohit Mishra· Journal of engineering and a...· 0 citations
To address the problem of the fault mode discrimination accuracy of marine rotating machinery under the conditions of noise interference and limited training samples, a fault diagnosis method based on a particle swarm optimization-driven kernelized cascade forest is proposed. This method introduces RBF kernel mapping in the hierarchical structure of the cascade forest and uses PSO to conduct global optimization of the key parameters of the kernel function. Based on the marine fan test platform driven by a three-phase asynchronous motor, the frequency domain features are extracted as the model input, and the predicted probability distribution is utilized during the hierarchical training process. The experimental results show that in a noise-free environment and a noisy environment with Gaussian white noise, the test accuracy of the diagnostic accuracy rate is 98.05% and 97.10%, respectively, with a performance decrease of only 0.95%; it still maintains stable recognition accuracy under the condition of small samples, demonstrating superior small-sample learning ability compared to the benchmark method. Compared with the forest-based baseline method, the proposed method can reduce the performance degradation caused by noise and effectively improve the diagnostic performance of the model under noise and small-sample conditions, and this method may become a potential solution for fault intelligent diagnosis in marine rotating machinery.
Unknown authors· Journal of Marine Science an...· 0 citations
It is confirmed that traditional machine learning models, optimized through manual feature engineering, can provide a ‘high-precision, low-risk’ solution for bearing fault diagnosis and offers significant reference value for the intelligent operation of aero-engines and other industrial equipment.
Qianxi Ye, Pengfang Gao· The 2026 International Confe...· 0 citations
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.
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
