Aug 2026· Journal of Dynamics Monitoring and Diagnostics· 0 citations· 32 references
TL;DR
A novel diagnosis method integrating FFT-VMD feature extraction with a Bi-TCN-Bi-GRU neural network, which maintains an exceptional diagnostic accuracy even under severe background noise.
Abstract
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.
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
The results show that SAMACNN outperforms both classical and advanced methods on the two datasets, demonstrating strong robustness and generalization capability in complex variable-condition measurement environments.
DaXin Li, Hong Wang, Hai Xue et al.· Engineering Research Express· 0 citations
Fault diagnosis of rolling element bearings (REBs) is crucial for ensuring the safety, reliability, and economic efficiency of modern industrial systems. However, conventional deep learning models often suffer from high computational costs and fixed receptive fields, which limit their deployment on resource-constrained edge devices. To address these issues, an adaptive variable-scale lightweight convolutional neural network (AVS-LCNN) is proposed. First, Gramian Angular Difference Field (GADF) coding is employed to transform one-dimensional vibration signals into time-frequency dual-channel images. Subsequently, depth-separable convolution is utilized to reconstruct the backbone network of AVS-LCNN, significantly reducing the number of model's parameters. To better capture the multi-scale characteristics of fault signals, a variable-scale feature extraction mechanism is developed based on the dilated convolution and the Atrous Spatial Pyramid Pooling (ASPP) module. Additionally, a sample-aware dynamic weighting strategy is introduced, in which a soft gating mechanism generates a weights α, enabling the model to automatically optimize its structure according to the complexity of the input samples. Experiments conducted on the HTBF and PU datasets show that AVS-LCNN achieves a diagnostic accuracy rate of over 99% with only 0.17M parameters, demonstrating a favorable balance among computational accuracy, robustness, and inference efficiency. These results indicate that the proposed method provide an effective solution for industrial edge applications.
Jia-Dong Meng, Zhao-An Hao, Hu-Tang Sang et al.· Measurement science and tech...· 0 citations
A rolling bearing fault diagnosis method based on multi-scale depthwise separable convolution (DSC) and a convolutional neural network-Transformer hybrid model (CNN-Transformer) is proposed to address the non-stationarity of fault signals and the difficulty of jointly capturing local and global features. First, continuous wavelet transform (CWT) converts one-dimensional vibration signals into two-dimensional time-frequency images to enhance fault representation. Then, multi-scale convolution (MSC) and DSC are introduced to extract local impulsive features and fault patterns at different scales with fewer parameters. A CNN-Transformer architecture is further developed, where convolutional neural network (CNN) captures local details and Transformer models global dependencies. In addition, pretraining-finetuning, data augmentation, label smoothing, and normal sample optimization are adopted to improve training stability and diagnostic performance. Experimental results show accuracies of 98.80% on the Xi’an Jiaotong University bearing dataset (XJTU-SY) and 100.00% on the Case Western Reserve University bearing dataset (CWRU), demonstrating strong discriminative ability, stability, and robustness.
Shuai Yang, Yan-Chao Chen, Yang Yu· Engineering Research Express· 0 citations
Bearings are critical components of rotating machinery, yet reliable fault diagnosis remains challenging under complex conditions due to signal non-stationarity and data scarcity. To address these issues, this paper proposes a diagnostic framework that combines generative-adversarial data augmentation with dual-branch time–frequency representation learning to improve feature quality and fault classification. Firstly, the bearing vibration signals are transformed into two-dimensional time-frequency representations by utilizing the continuous wavelet transform. Subsequently, a multi-attention conditional deep convolutional generative adversarial network (MCDCGAN) is employed for conditional augmentation under class imbalance, integrating attention mechanisms and stabilization strategies to generate more reliable samples for minority fault classes. Finally, a dual-branch parallel time-frequency attention network (DPTAN) is designed to jointly learn temporal and spectral feature representations and then fuse them for fault classification. Experimental results on the CWRU and HIT datasets demonstrate that the proposed method achieves better performance than baseline models and maintains robustness under data imbalance and noisy conditions.
Sen Zhang, Lei Yan, Zhaodong Liu et al.· Journal of Measurements in E...· 0 citations
A hybrid deep learning architecture is proposed for robust vibration-based fault diagnosis in industrial machinery by jointly modeling time-domain, frequency-domain, and temporal dynamics, enabling coherent cross-domain interaction and robust fault characterization.
Canan Taştimur· Information Technology and C...· 0 citations
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