In recent years, deep learning technology has been widely applied to bearing fault diagnosis, achieving significant progress. However, noise often masks fault characteristics, making feature extraction difficult and reducing diagnostic accuracy, which in turn hinders practical application. Therefore, this paper proposes a fault diagnosis model based on a gating mechanism and multi-scale feature fusion (multi-scale distributed convolution (MSDC)-Dense DGated convolutional neural network (CNN)). This model enhances noise resistance through a two-stage feature learning architecture: The first stage employs an MSDC module, which incorporates three parallel group convolutions with distinct dilation rates. This simultaneously filters noise while capturing multi-scale temporal features. Group CNN reduces parameter redundancy and strengthens local feature extraction. The second stage introduces a Dense DGated CNN block. This block dynamically suppresses noise interference through a product operation between feature branches and dilation-gated branches. A 1–1 convolution layer is added after convolutions with high dilation rates to mitigate the grid effect. Dense connections are employed to strengthen feature correlations. Additionally, the model incorporates a squeeze and excitation module that utilizes channel attention to amplify critical fault features while suppressing noise-dominated redundant information adaptively. The proposed model was validated on the CWRU and self-test datasets under various noise conditions. Experimental results demonstrate that the model achieves a diagnostic accuracy of 89.70% even in high-noise environments with a signal-to-noise ratio of −10 dB. Characterized by minimal parameters and strong anti-interference capabilities, it is well-suited for fault detection tasks in industrial time-series signals that are heavily influenced by substantial noise interference.

Bearings, as core components of mechanical equipment, play a critical role in ensuring equipment safety and reliability. Early fault detection holds significant importance. Addressing the challenges of insufficient robustness in bearing fault diagnosis under industrial high-noise conditions and the difficulty of extracting fault features from a single modality, this study proposes a three-channel multimodal fault diagnosis method that integrates a Convolutional Auto-Encoder (CAE) with a dual attention mechanism (M-CNNBiAM). This approach provides an effective technical solution for the precise diagnosis of bearing faults in high-noise environments. To suppress substantial noise interference, a CAE denoising module was designed to filter out intense noise, providing high-quality input for subsequent diagnostic networks. To address the limitations of single-modal feature extraction and restricted generalization capabilities, a three-channel time–frequency signal joint diagnosis model combining the Continuous Wavelet Transform (CWT) with an attention mechanism was proposed. This approach enables deep mining and efficient fusion of multi-domain features, thereby enhancing fault diagnosis accuracy and generalization capabilities. Experimental results demonstrate that the designed CAE module maintains excellent noise reduction performance even under −10 dB strong noise conditions. When combined with the proposed diagnostic model, it achieves an average diagnostic accuracy of 98% across both the CWRU and self-test datasets, demonstrating outstanding diagnostic precision. Furthermore, under −4 dB noise conditions, it achieves a 94% diagnostic accuracy even without relying on the CAE denoising module. With a single training cycle taking only 6.8 s, it balances training efficiency and diagnostic performance, making it well-suited for real-time, reliable bearing fault diagnosis in industrial environments with high noise levels.

As a core component of mechanical equipment, the operational status of bearings directly determines equipment safety, making early fault diagnosis critically important. However, bearing vibration signals are susceptible to substantial noise interference and exhibit both nonlinear and non-stationary characteristics, rendering traditional single-mode diagnostic methods ineffective at extracting fault features. Therefore, this paper proposes a three-channel multimodal fault diagnosis network (M-CNNBiAM) integrated with a convolutional autoencoder (CAE). Based on a convolutional neural network (CNN) architecture, this network employs CAE for signal denoising, utilizes continuous wavelet transform (CWT) to construct time-frequency features, and incorporates dual enhancement modules: convolutional attention (CBAM) and window attention (S-W-MSA).On one hand, it extracts complementary features from the raw vibration signal and the wavelet transform frequency domain signal, fusing them at the channel dimension. On the other hand, it embeds Shifted Window Attention (SW-MSA) and Window Self-Attention (W-MSA) between convolutional layers to capture global-local features. Combined with CBAM to enhance fault location attention, it mitigates the vanishing gradient problem through residual connections, enabling the extraction of frequency domain features. To address the characteristics of one-dimensional time-series signals, a bidirectional gated recurrent unit (BiGRU) is introduced to collaborate with CNN for extracting temporal features. Experiments demonstrate that on the West China University public dataset and self-test dataset, M-CNNBiAM achieves an average diagnostic accuracy of 95.84% under -10dB high-noise conditions, outperforming comparative methods and validating its superior performance in complex noise environments.

As critical components in rotating machinery, bearing health status directly affects the operational safety of equipment and personnel. Therefore, effective bearing condition monitoring is of great importance. However, challenges such as scarce labeled samples, varying operating conditions, and strong noise interference severely restrict the performance of high-precision fault diagnosis. To address these issues, this paper proposes a fault diagnosis method that combines maximum mean discrepancy and a domain-adversarial neural network to reduce feature discrepancies under variable speed and load conditions. High-confidence pseudo-labels are adopted to improve diagnostic performance under semi-supervised settings. Based on the above strategies, a unified loss function is constructed and a high-precision bearing-fault diagnosis framework adaptable to varying working conditions is established. Meanwhile, a tailored convolutional auto-encoder (CAE) is designed for strong-noise environments to denoise vibration signals and provide high-quality inputs for the network. Experimental results demonstrate that, with ResNet14 as the backbone, the proposed feature alignment method achieves diagnostic accuracy exceeding 98% on both the Case Western Reserve University dataset and the self-built test dataset, even when only 5% of target-domain data are labeled. The constructed CAE module maintains reliable denoising performance even under a high noise level of −10 dB. The entire model contains 1531 973 parameters and requires only 3 s per training epoch. Accordingly, the proposed method is well-suited for semi-supervised domain-adaptive bearing fault diagnosis under real industrial scenarios.

Rolling bearings inevitably generate noise during operation, which reduces diagnostic accuracy. In this paper, we propose a center-frequency-learning Variational Modal Decomposition (VMD) combined with a Convolutional Autoencoder (CAE) (CAEVMD) to process noisy signals and extract fault features, using a multiscale Convolutional Neural Network (CNN) for bearing fault diagnosis. First, the noisy signal is fed into the VMD for decomposition. The decomposed modal signal is sparsely penalized to suppress the modal noise. The sparsely penalized modal component is encoded, compressed, decoded, and reconstructed. The reconstructed output signal shows reduced noise. Secondly, the fault features are extracted using a multiscale dilated Convolutional Neural Network, combined with the Spatial Attention Mechanism (SAM) and the Channel Attention Mechanism (CAM), for fault diagnosis. Validated on the Western Reserve University bearing dataset and the simulated bearing experimental dataset, the experimental results show that the proposed network model achieves high diagnostic accuracy across different noise conditions, outperforms the comparative methods, and demonstrates high accuracy, robustness, and good generalization.

As critical components of mechanical equipment, the health condition of bearings directly determines the stable operation and operational safety of machinery. Thus, timely early fault diagnosis of bearings is of great practical significance. Nevertheless, collected vibration signals are frequently contaminated by strong ambient noise, which hinders effective extraction of discriminative fault features and degrades diagnosis accuracy. To tackle this problem, this paper proposes a multi-scale noise-robust residual network built with dilated structures to boost diagnosis accuracy under high-noise environments. Specifically, stacked dilated convolutions are embedded inside residual bottleneck blocks to enlarge the receptive field of shallow networks during fault feature extraction, and dimensionality reduction operations are adopted to suppress noise interference. Stacked such dilated residual blocks are assembled to construct a dedicated noise-robust module named DRes-Block. Furthermore, a channel attention module is deployed after the noise-robust module to strengthen the weight responses of feature channels highly correlated with faults, which further improves the anti-noise capability of the module. To fully capture comprehensive fault features, a multi-scale feature extraction module is designed, which adopts distinct dilation rates on parallel branches to mine multi-scale inherent characteristics of fault vibration signals. This module is integrated to construct an end-to-end bearing fault diagnosis model, namely DResB-MDCNN. Experimental results demonstrate that the proposed model achieves diagnosis accuracy higher than 95% under strong noise with an SNR of −8 dB on both the CWRU bearing dataset and self-simulated bearing dataset. Even under the extreme noise condition of SNR = − 10 dB, the model still reaches an accuracy of 87.07%, outperforming ResNet18, VGG16, CNN-TCN and WDCNN-BiGRU by 2.53%, 29.45%, 4.95% and 11.93% respectively. Based on the data sampling and partitioning strategy proposed in this paper and the experimental platform used, each training epoch only takes 3.4 s. The total number of trainable parameters of the model is 2326 438, with a storage size of approximately 8.87 MB, which renders the model lightweight. Therefore, the DResB-MDCNN model proposed in this paper possesses prominent comprehensive advantages for bearing fault diagnosis under heavy noise interference.