Uncertainty-guided denoising bi-classifier adversarial domain adaptation network for cross-domain fault diagnosis

Intelligent fault diagnosis is crucial for ensuring the safety and reliability of modern industrial systems. However, the performance of deep learning models often significantly degrades due to the domain shift between training and testing data. Domain Adaptation (DA) methods, particularly bi-classifier adversarial networks, have proven effective in transferring knowledge from a labeled source domain to an unlabeled target domain. However, existing approaches often pay insufficient attention to target sample prediction accuracy, resulting in reduced feature discriminability and generalization. Additionally, due to the absence of labeled target data, most approaches rely on pseudo-labels, which are often noisy and unreliable, especially in the early stages of training. To address these issues, this paper proposes a novel uncertainty-guided denoising bi-classifier adversarial domain adaptation network (UGDBAN) for cross-domain fault diagnosis. Specifically, a feature generator based on Transformer layers is designed to capture long-range dependencies and local features. To mitigate the impact of noisy pseudo-labels, an uncertainty-based denoising pseudo-labeling mechanism is introduced to enhance the discriminability of features by redefining pseudo-labels and dynamically selecting high-confidence samples as clean samples. Building upon this denoised pseudo-label set, a Dirichlet uncertainty estimation-based class prototype alignment strategy is proposed to align domain features at the class level by selecting low-uncertainty samples representative of each class as prototypes. Extensive experiments demonstrate the effectiveness of UGDBAN, and comparative results with mainstream methods highlight its superiority.