Domain Adversarial Generalized Networks for Real-Time Fault Diagnosis of Rolling Bearings Without the Need for Target Domain Samples

In the Industrial Internet of Things (IIoT), detecting and identifying faults in machinery is crucial as it helps to eliminate potential failures and reduce losses caused by these faults. Deep learning models employing domain adaptation methods have proven highly effective for diagnosing faults in bearings. These models, however, require the incorporation of examples from the target domain during training, resulting in an excessive dependence on information from the target domain. In real-world applications, target domain data sources are often discrete, diverse, and inaccessible, requiring the model to be re-trained for each diagnosis and thus hindering real-time diagnostics. To tackle this problem, this research introduces adversarial generalized models aimed at real-time failure detection of rolling elements without requiring samples from the target domain. First, this model enhances domain-invariant feature extraction through multi-scale joint weighting augmentation. Additionally, it utilizes random norm mutual coupling to determine the Wasserstein distance for assessing domain variation, thereby improving convergence and operational efficiency. To further enhance domain confusion ability and robustness, the Label Smoothing Regularization approach is introduced for calculating model class classification loss. Finally, tests were performed using three publicly available datasets and one private dataset, achieving optimal accuracies of 98.08%, 85.40%, and 91.90% for the public datasets, along with a 97.69% accuracy for the private dataset. These results confirm the robustness and effectiveness of the method introduced in this study.