Jul 2026· Structural Health Monitoring· 0 citations· 38 references
TL;DR
An ensemble attention-based residual convolutional neural network optimized by the vortex search algorithm can effectively overcome the limitations of individual models and achieve superior fault identification performance than existing methods under many types of severe conditions.
Abstract
Rolling bearings are essential components in mechanical systems, whose fault diagnosis is vital for operational efficiency. But in real industrial environments, the harsh conditions, including noise, missing data, and compound faults, severely limit the diagnostic performance of the algorithm. Thus, we propose an ensemble attention-based residual convolutional neural network (CNN) optimized by the vortex search algorithm. First, a new residual CNN with the improved residual structure, the separable convolution, and the global average pooling layer is designed to extract features from the vibration signals automatically. Second, a residual cooperative attention mechanism is presented. To guarantee the difference between the base models, different base models are constructed employing multiple convolutional kernels, activation functions, as well as attention mechanisms, respectively. And different training sets are allocated to each base model by Bootstrap. Third, a new exponential threshold decision fusion strategy is put forward to achieve ensemble learning. Eventually, the vortex search algorithm is employed to optimize the parameters of the decision fusion strategy. The noise, missing data, and compound fault datasets constructed separately using data from two rolling bearing experiments reveal that the proposed ensemble model can effectively overcome the limitations of individual models and achieve superior fault identification performance than existing methods under many types of severe conditions.
A robust and noise-resilient bearing fault diagnosis framework that integrates advanced signal processing with hybrid deep learning techniques is presented, demonstrating strong robustness and generalization capability.
Sujit Kumar, Manish Kumar, Bam Bahadur Sinha· International Journal of Dyn...· 0 citations
A dual-channel attention and feature selection-based fault diagnosis method for rolling bearings is proposed, and a corresponding digital twin-based interaction system is further developed.
Erpeng Wang, Zhaoze Sun, Jian Wang et al.· Engineering Research Express· 0 citations
To address the degradation of cross-condition diagnostic performance caused by feature-scale drift in rolling bearing vibration signals under variable operating conditions, this paper proposes a spectral-guided adaptive multi-scale convolutional neural network (SAMACNN). First, PSD sequences and time-frequency features are introduced as dual-stream inputs. While the time-frequency main branch extracts local information, the Spectral Transformer spectral bypass branch captures long-range dependencies in harmonic structures. Second, dynamic gating weights are generated for the multi-scale convolutional branches, enabling sample-conditioned multi-scale feature selection and fusion and alleviating scale mismatch caused by fixed receptive fields and static fusion. Finally, data collected from two bearing fault simulation test rigs are used to verify the effectiveness and superiority of the proposed algorithm. The experimental results show that the proposed SAMACNN method achieves average accuracies of 97.16% and 97.56% on the two datasets, respectively, outperforming the ablation variants and demonstrating strong robustness and generalization capability in complex variable-condition measurement environments.
DaXin Li, Wang Hong, Hai Xue et al.· Engineering Research Express· 0 citations
To tackle the persistent challenges of low bearing fault diagnosis accuracy—specifically the difficulty of extracting faint fault features under complex, variable operating conditions and strong background noise, as well as the tendency to lose deep temporal dependencies—this paper proposes a novel diagnosis method integrating FFT-VMD feature extraction with a Bi-TCN-Bi-GRU neural network. First, to overcome the heuristic parameter selection and frequency-band aliasing inherent in traditional Variational Mode Decomposition (VMD), the Fast Fourier Transform (FFT) is introduced. By extracting global spectral prior information, the FFT guides the VMD to perform adaptive decomposition and effective denoising of non-stationary vibration signals. Subsequently, a dual-path Bi-TCN-Bi-GRU diagnostic model is constructed. The dilated causal convolution mechanism of the Bidirectional Temporal Convolutional Network (Bi-TCN) is utilized to extract deep local spatial features, while the Bidirectional Gated Recurrent Unit (Bi-GRU) is integrated to deeply mine the forward and backward dynamic evolutionary dependencies within the sequence. Experimental results demonstrate that even under severe background noise with a signal-to-noise ratio (SNR) of -2 dB, the proposed method maintains an exceptional diagnostic accuracy of 98.21% on a self-built bearing dataset and 99.90% on the Southeast University (SEU) dataset. This study provides a highly robust and promising solution for enhancing bearing safety and predictive maintenance in complex industrial environments.
This smart fault diagnosis method based on the Time Convolution Network - Bidirectional Gated Recurrent Unit - Attention Model (TCN-BiGRU-Attention) can achieve high-precision and stable fault diagnosis for rolling bearings, providing an effective intelligent diagnosis solution for engineering applications.
Ming-Li Li, Zhu Yuan· Frontiers of Mechanical Engi...· 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
