Bearing Single-Source Domain Generalization Fault Diagnosis Method Based on Adaptive Frequency-Domain Augmentation and Unsupervised Contrastive Learning

Cross-Domain Bearing Fault Diagnosis Using Quantile-Based Domain Adaptation and Random Subspace Ensemble

Prior-augmented measurement-signal fusion using a dual-branch cross-attention network for cross-domain bearing fault diagnosis

Cross-machine bearing fault diagnosis is strongly affected by inconsistencies in vibration measurement conditions, including rotational speed, sampling frequency, and structural transmission paths. These factors cause speed-induced fault-frequency drift and cross-domain distribution discrepancies, making features learned from source-domain measurements unreliable in target-domain scenarios. Existing transfer learning (TL) methods are predominantly data-driven and insufficiently exploit mechanism-related prior information, which limits their interpretability and cross-machine generalization. To address these challenges, this paper proposes a prior-augmented dual-branch cross-attention network, termed PA-DCA Net, for cross-domain adaptive bearing fault diagnosis. First, a multi-scale S-transform with channel-weighted fusion is used to construct informative time-frequency representations from vibration signals. Meanwhile, a speed-normalized prior feature is introduced at the input-feature level to encode the relative rotational-speed discrepancy between the current operating condition and the source-domain reference condition. This prior feature is combined with eight conventional time-domain statistical features to form a statistical-prior feature vector. Second, an image-statistical dual-branch network is constructed, in which the image branch extracts deep time-frequency features and the statistical branch maps the statistical-prior vector into a high-dimensional representation. Multi-head cross-attention is then employed to achieve directed feature interaction between the two modalities. Third, a progressive TL framework integrating source-domain supervised pretraining, few-shot target-domain fine-tuning, CORAL, multi-kernel maximum mean discrepancy, and FixMatch-based consistency regularization is adopted. The proposed method is validated on five cross-domain tasks constructed from three public bearing datasets. PA-DCA Net achieves average accuracies of 97.10% and 96.92% on the Case Western Reserve University (CWRU)-to-Jiangnan University and CWRU-to-Huazhong University of Science and Technology cross-machine tasks, respectively, outperforming several representative transfer-learning baselines.

A Study on Domain Generalization Methods for Rolling Bearing Fault Diagnosis Based on Mean Differences and Domain Adversarial Learning

To address the issue that intelligent fault diagnosis models for rolling bearings are susceptible to the effects of load and speed variations, as well as shifts in data distribution, when applied across different operating conditions, this paper proposes a fault diagnosis domain generalization method that combines mean difference constraints with domain adversarial learning. Taking raw one-dimensional vibration signals as input, this method first employs a multiscale one-dimensional convolutional network to extract fault impact features and periodic features across different time scales; Subsequently, mean difference constraints between source domains are introduced in the feature space to reduce the offset in the centers of feature distributions across different source operating conditions; simultaneously, a domain-adversarial discriminator based on gradient inversion layers is constructed to weaken the operating condition discriminative information in the feature representations, thereby prompting the model to learn domain-invariant features that possess both fault class discriminability and operating condition insensitivity. Experiments were conducted using two rolling bearing datasets from Case Western Reserve University (CWRU) and Paderborn University (PU) to establish a leave-one-out cross-condition diagnosis task. The results show that the proposed method achieves an average accuracy of 99.08% ± 0.19% on the CWRU dataset, and an average accuracy of 91.86% ± 0.34% on the PU dataset, both outperforming comparison methods such as SVM, 1D-CNN, ResNet1D, TCN, CORAL, MMD, and DANN. Furthermore, a Welch’s t-test based on summary statistics indicates that the improvement over the best baseline method is statistically significant. Ablation experiments and parameter sensitivity analysis further validate the effectiveness of multiscale feature extraction, mean difference constraints, and domain adversarial learning in enhancing the model’s generalization capability across different operating conditions.

PGDS-CLNet: A robust bearing fault diagnosis method under varying loads and strong noise

A novel intelligent fault diagnosis framework, termed PGDS-CLNet, is proposed in this study, which is an end-to-end trainable diagnostic network after standard signal normalization and segmentation.

Padé Approximant Neural Networks as Feature Extractors for Unsupervised Domain Adaptation in Bearing Fault Diagnosis

These results establish PadéNet-enhanced UDA as an accurate, broadly applicable approach for robust bearing fault diagnosis under varying operating conditions, with a reduced parameter count suited to resource-constrained embedded platforms.

Hybrid Loss-Driven Source-Free Domain Adaptation with Post-hoc Explainability for Bearing Fault Diagnosis

A novel and robust Source-Free Domain Adaptation (SFDA) framework equipped with post-hoc explainability for bearing fault diagnosis is proposed, and a post-hoc visualization mechanism is incorporated to explain the model's decision-making process, enhancing the transparency and credibility of the diagnosis for practical industrial deployment.