To address the problems of feature extraction, cost of obtaining labeled samples, and large differences in domain distribution in bearing fault diagnosis on variable operating conditions, an unsupervised domain-adaptive bearing fault diagnosis method based on migration learning using MSACNN-IJMMD-DANN (multi-scale and attention-based convolutional neural network, MSACNN, improved joint maximum mean discrepancy, IJMMD, domain adversarial neural network, DANN) is proposed. Firstly, in order to extract fault-type features from the source domain and target domain, this paper establishes a MSACNN based on multi-scale and attention mechanisms. Secondly, to reduce the feature distribution difference between the source and target domains and address the issue of domain distribution differences, the joint maximum mean discrepancy and correlation alignment approaches are used to create the metric criterion. Then, the adversarial loss mechanism in DANN is introduced to reduce the interference of weakly correlated domain features for better fault diagnosis and identification. Finally, the method is validated using bearing datasets from Case Western Reserve University, Jiangnan University, and our laboratory. The experimental results demonstrated that the method achieved higher accuracy across different migration tasks, providing an effective solution for bearing fault diagnosis in industrial environments with varying operating conditions.

Noises on the field can affect the electromechanical system characteristics in the bearing fault diagnostic process. This paper presents a deep learning-based fault-diagnosis model DRSN–GCE (Deep Relative Shrinkage Network with Gated Convolutions and Enhancements), which is designed to deal with noise and improve noise resistance. In the first step, the data are preprocessed by adding different noises with different ratios of signal to noise and different frequencies to the vibration signals. This simulates the field noise environments. The continuous wavelet transformation (CWT), which converts the time-series signal from one dimension to a time-frequency two-dimensional image, provides rich data input for the deep learning model. Secondly, a convolutional gated layer is added to the deep residual network (DRSN), which suppresses the noise interference. The residual connection structure has also been improved in order to improve the transfer of features. In complex signals, the Gated Convolutional Shrinkage Module is used to improve feature extraction and suppress noise. The experiments on the Case Western Reserve University bearing dataset show that the DRSN–GCE exhibits high diagnostic accuracy and strong noise immunity in various noise environments such as Gauss, Laplace, Salt-and-Pepper, and Poisson. DRSN–GCE is superior to other deep learning models in terms of noise suppression, fault detection accuracy, and rolling bearing fault diagnoses in noisy environments.

Rolling bearing vibration signals are often severely affected by strong external noise, which can obscure fault-related features and hinder accurate diagnosis. To address this challenge, this paper proposes an enhanced Deep Residual Shrinkage Network with Dynamic Convolution and Selective Kernel Attention (DDRSN-SKA). First, one-dimensional vibration signals are converted into two-dimensional time frequency images using the Continuous Wavelet Transform (CWT), providing richer input representations. Then, a dynamic convolution module is introduced to adaptively adjust kernel weights based on the input, enabling the network to better extract salient features. To improve feature discrimination, an Selective Kernel Attention (SKAttention) module is incorporated into the intermediate layers of the network. By applying a multi-receptive field channel attention mechanism, the network can emphasize critical information and suppress irrelevant features. The final classification layer determines the fault types. Experiments conducted on both the Case Western Reserve University (CWRU) dataset and a laboratory-collected bearing dataset demonstrate that DDRSN-SKA achieves diagnostic accuracies of 98.44% and 94.44% under −8 dB Gaussian and Laplace noise, respectively. These results confirm the model’s strong noise robustness and its suitability for fault diagnosis in noisy industrial environments.

Under complex industrial operating conditions with variable speeds, variable loads and high-level background noise, vibration-based rolling bearing fault diagnosis is challenging as noise obscures fault-related features. Existing fault diagnosis methods rely primarily on fault labels for feature learning and fail to fully exploit environmental information embedded in noise-level labels. To address this issue, to the authors’ knowledge, this paper is the first to treat noise-level identification as an auxiliary classification task in bearing fault diagnosis, and a multi-task multi-scale channel-mixed residual network is accordingly proposed. Taking fault classification as the main task and noise-level identification as the auxiliary task, the approach transforms noise-level information into effective auxiliary supervisory signals. A multi-scale channel-mixed residual network acts as the shared feature-extraction backbone. A hierarchical parameter-sharing architecture balances cross-task feature sharing and task-specific representation. A channel–spatial attention module embedded with the Kolmogorov–Arnold network enhances task-relevant features, while a dynamic loss-weighting strategy coordinates joint optimization of the two tasks. Experiments conducted on the public CWRU dataset and laboratory-tested bearing dataset under diverse noise, load and rotational-speed conditions show that noise-level auxiliary supervision raises average fault classification accuracy by 1.85 percentage points over single-task models. Compared with mainstream multi-task diagnostic methods including multi-task attention convolutional network, multi-task ConvFormer and multi-task one-dimensional convolutional neural network, the proposed method achieves higher diagnostic accuracy with smaller performance degradation under fluctuating operating conditions, demonstrating favorable robustness for practical condition-monitoring applications.