Parametric Generalized Adaptive Moment Features (PG-AMF) for Bearing Fault Diagnosis and Machine Health Monitoring

Feature Disentanglement Augmented Network for Vibration-Based Fault Diagnosis of Rotating Machinery Under Unknown 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.

Enhanced Rotating Machinery Fault Diagnosis Using Holo-Hilbert Spectrum Analysis and Machine Learning

Reliable fault diagnosis in rotating machinery is challenging due to the nonlinear and non-stationary nature of vibration signals. Although time–frequency analysis is widely used, it cannot capture the cross-scale coupling between amplitude-modulated (AM) and frequency-modulated (FM) components that carry essential diagnostic information. This study applies Holo-Hilbert Spectrum Analysis (HHSA) to extract amplitude–frequency modulation features and integrates them with six machine learning classifiers to identify four fault conditions. Random Forest, K-Nearest Neighbors, and Logistic Regression achieve accuracies of up to 99.95%, yielding higher accuracy than Fast Fourier Transform-based features. The proposed framework employs an HHSA-based feature extraction pipeline that effectively captures AM–FM coupling in nonlinear vibration signals. It also provides higher discriminative capability than traditional spectral approaches and maintains robustness across multiple classifiers. This method offers high diagnostic accuracy and strong potential for industrial predictive maintenance. Future work will focus on improving computational efficiency and evaluating the framework under more diverse and realistic operating conditions. Received: 10 September 2025 | Revised: 20 April 2026 | Accepted: 10 June 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The VBL-VA001 datasets that support the findings of this study are openly available at https://doi.org/10.1007/s42417-023-00959-9, reference number [44]. Author Contribution Statement Van-Trung Nguyen: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing - original draft, Writing - review & editing, Visualization. Ba-Tan Le: Investigation, Data curation, Writing - original draft, Writing - review & editing. Van-Phuong Dao: Methodology, Validation, Writing - review & editing.

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

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.

Multi-Class Fault Detection and Diagnosis of Rolling Bearings: a Machine Learning Approach

Hierarchical Approach to Rolling Bearing Failure Detection Using Spectrogram Images Classification

Early detection of rolling bearing faults is essential for preventing failures in rotating machinery. Although vibration-based analysis is widely used for fault diagnosis, it requires additional sensors, such as accelerometers, which increases system cost and complexity. Motor current signals, inherently available in industrial drive systems, offer a cost-effective alternative; however, their indirect sensitivity to mechanical defects makes reliable fault diagnosis challenging. This paper proposes a hierarchical motor current-based diagnostic framework that integrates domain knowledge with data-driven image analysis. Raw current signals are transformed into time-frequency spectrograms, which provide enhanced representations of faults compared to pure time or frequency-domain features. The proposed approach consists of three stages. First, bearing health is characterized using a rule-based method to compute the characteristic frequencies associated with different fault types, incorporating domain knowledge into the diagnosis process. Second, this information is used to guide a hierarchical classification strategy that separates health assessment from fault localization. Finally, global image descriptors extracted from the spectrograms are employed to classify the bearing condition as healthy or faulty and to identify the fault location (internal or external). By explicitly combining physics-informed fault characterization with image-based machine learning, the proposed approach improves interpretability while maintaining competitive diagnostic performance. The effectiveness of the method is validated on a benchmark bearing dataset, demonstrating its potential for cost-effective industrial condition monitoring.

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.