Experiments on the CWRU bearing dataset across seven signal-to-noise ratio levels demonstrate that the proposed method achieves 99.75% accuracy under clean conditions and maintains 92.50% at -4 dB SNR, representing a 7.25 percentage-point improvement over the single-domain baseline.
Abstract
To address the degradation of bearing fault diagnosis accuracy under strong noise, this paper proposes a time-frequency dual-domain multi-scale convolutional neural network. The time-domain branch employs three parallel convolutional kernels to capture multi-scale impulse features, while the frequency-domain branch applies the Fast Fourier Transform to extract noise-robust spectral structure information. Features from both branches are fused for fault classification, yielding a compact model of 110,122 parameters. Experiments on the CWRU bearing dataset across seven signal-to-noise ratio levels demonstrate that the proposed method achieves 99.75% accuracy under clean conditions and maintains 92.50% at -4 dB SNR, representing a 7.25 percentage-point improvement over the single-domain baseline with monotonically increasing gains under stronger noise. Ablation experiments validate the independent performance contributions of the time-domain multi-scale branch and the frequency-domain branch. Comparative experiments against WDCNN, DRSN-CW, MCNN, and 1D-LeNet confirm the superiority of the proposed method under strong noise conditions.
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
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
This paper proposes a highly efficient framework, the Frequency-domain Circulant Attention Vision Transformer (FC-ViT), for robust rotating machinery monitoring, and demonstrates superior noise immunity and cross-load generalization.
Zhijun Teng, Ji-Qiu Li, Mingyang Sun et al.· IEEE Access· 0 citations
In industrial scenarios, strong background noise can easily overwhelm the weak impulsive signatures of gearbox faults, leading to time-domain waveform distortion and frequency-domain spectral aliasing, which in turn degrades the feature extraction capability of conventional diagnostic models and significantly reduces their diagnostic accuracy and robustness. To overcome this limitation, a dual-domain generative–discriminative fusion network (DDGF-Net) is proposed for robust gearbox fault diagnosis under strong noise interference. The proposed framework consists of three collaborative components. Firstly, an improved conditional variational autoencoder (CVAE) integrating soft-threshold shrinkage and spectral consistency constraints is designed to perform joint time–frequency denoising and signal reconstruction, thereby preserving subtle fault characteristics while effectively suppressing noise. Secondly, parallel time-domain and frequency-domain encoding branches are constructed to extract transient fault impulses and fault characteristic frequencies, respectively, compensating for the inadequacy of single-domain feature representations. Thirdly, a lightweight bidirectional cross-attention mechanism is introduced to overcome the limitations of conventional fixed-weight fusion strategies, enabling dynamic adjustment of the interaction weights between dual-domain features according to the instantaneous noise intensity, thus maximizing the complementary value of cross-domain features. Extensive comparative experiments conducted on the Southeast University(SEU) gearbox dataset demonstrate that DDGF-Net achieves superior diagnostic performance and noise robustness over eight representative methods, particularly under strong noise interference, thereby validating its effectiveness and superiority in harsh diagnostic scenarios.
A four-branch multi-modal unsupervised domain-adaptive fault diagnosis framework, termed CRG-DA Net, based on ConvNeXt and ResNet1D, aimed at enabling cross-condition fault diagnosis under unlabeled target data is proposed.
Zhihao Zhao, Li Xu, Jingjing Cai et al.· Measurement and control (Lon...· 1 citation
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
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