Aug 2026· Advanced Engineering&PrecisionManufacturing· Vol 2, pp. 1-23· 0 citations
Abstract
To address the insufficient generalization capability of rolling bearing fault diagnosis models under complex operating conditions such as variable rotational speed, variable load, and variable radial force, this paper proposes a domain generalization fault diagnosis method based on Mamba feature extraction and causal generalization loss. First, the raw vibration signals are standardized and segmented using a sliding window strategy. Then, the selective state space model Mamba is employed to model long-range temporal dependencies and local dynamic variations in fault impact signals. Subsequently, from the perspective of causal invariance, generalization constraints are constructed by treating stable representations related to fault categories as causal features, while regarding amplitude variations, noise disturbances, and speed fluctuations induced by operating-condition changes as non-causal factors. A causal generalization loss consisting of class-conditional causal prototype consistency loss and feature decoupling regularization is designed to enhance the model’s adaptability to unseen operating conditions. Experiments are conducted on the Paderborn University (PU) and JNU datasets, where four operating conditions are regarded as four domains to construct cross-condition diagnosis tasks under a leave-one-condition-out evaluation protocol. The proposed method is compared with support vector machine (SVM), one-dimensional convolutional neural network (1D-CNN), one-dimensional residual network (ResNet1D), temporal convolutional network (TCN), correlation alignment (CORAL), domain-adversarial neural network (DANN), maximum mean discrepancy (MMD), and vanilla Mamba. Experimental results show that the proposed method achieves an average accuracy of 93.56% on the PU dataset and 96.28% on the JNU dataset, outperforming all comparison methods and further demonstrating its effectiveness.
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.
TitanDiag, a recently proposed architecture for long-context language modelling, tackles a similar challenge of maintaining performance across varying contexts by adapting this mechanism to fault diagnosis for rolling bearings.
Bingcong Li· Advances in Engineering Inno...· 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 fault diagnosis of rotating machinery based on vibration measurements is essential for reliable condition monitoring and predictive maintenance in industrial systems. However, conventional data-driven diagnostic models often experience significant performance degradation under varying operating conditions, primarily due to distribution shifts in the acquired vibration signals. This article proposes a feature disentanglement augmented network (FDAN) to improve the robustness and generalization capability of vibration-based fault diagnosis for rotating machinery to unseen target domains. In FDAN, a disentanglement network is employed to explicitly separate the input features into domain-invariant and domain-specific components. An independently trained attention module is further introduced to identify the most classification-relevant components, which are subsequently used for augmentation to enrich the diversity of the learned representations. The proposed method requires no test-condition data during training, making it well-suited for industrial scenarios. Extensive experiments conducted on the widely used DIRG and SDUST fault datasets demonstrate that FDAN consistently outperforms several domain generalization (DG) approaches, achieving significant improvements in diagnostic accuracy under varying working conditions. Additional validations, including feature analysis and ablation studies, confirm the effectiveness and necessity of each component. These results demonstrate the potential of FDAN as an effective and deployable solution for robust intelligent fault diagnosis in vibration measurement-based monitoring of rotating machinery in industrial applications.
Yixuan Shan, Peixuan Ding, Xilian Yang et al.· IEEE Transactions on Instrum...· 0 citations
Fault diagnosis of rolling bearings is crucial for operational safety. However, the scarcity of labeled data and significant domain shifts are two key challenges. Existing studies neglect robustness to physical disturbances and the interpretability of diagnostic decisions. To address these issues, this paper proposes a physics-informed cross-domain fault diagnosis framework. First, based on bearing fault modulation mechanisms, multi-domain features are extracted. A physics-regularized feature selection strategy combining Sparse Group Lasso with stability selection and Random Forest is used to retain fault-relevant features. Recognizing that velocity variations are a major source of domain shifts, order analysis is introduced as a physics-prior alignment method. Deep CORAL and pseudo-label self-training are combined for unsupervised knowledge transfer. Finally, multi-level interpretability analysis is embedded to quantify domain shift, track adaptation geometry and explain final decisions. Two case studies demonstrate its robustness, trustworthiness and engineering applicability.
Yi Xie, Ruyang Zheng, Xuepeng Guo et al.· Eksploatacja I Niezawodnosc-...· 0 citations
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