A Gaussian-guided adversarial adaptation transfer network for rolling bearing fault diagnosis

Most current unsupervised domain networks try to alleviate domain shifts by only considering the difference between source domain and target domain caused by the classifier, without considering task-specific decision boundaries between categories. In addition, these networks aim to completely align data distributions, which is difficult because each domain has its characteristics. In light of these issues, we develop a Gaussian-guided adversarial adaptation transfer network (GAATN) for bearing fault diagnosis. Specifically, GAATN introduces a Gaussian-guided distribution alignment strategy to make the data distribution of two domains close to the Gaussian distribution to reduce data distribution discrepancies. Furthermore, GAATN adopts a novel adversarial training mechanism for domain adaptation, which designs two task-specific classifiers to identify target data to consider the relationship between target data and category boundaries. Massive experimental results prove that the superiority and robustness of the proposed method outperform existing popular methods.