Most data-driven methods for fault diagnostics rely on the assumption of independently and
identically distributed data of training and testing. However, domain shift between the …

Distribution shift significantly hampers the performance of deep fault diagnostic models in
real-world applications, prompting an increased focus on transfer learning-based fault …

Effective fault diagnosis under unknown operating conditions (also known as domains) is
crucial for ensuring the reliability and performance of mechanical systems. However, fault …

With the significant advancement of intelligent fault diagnosis (FD) technology in recent
years, federated learning methodologies, which employ data from multiple devices for …

Aiming at the problem of poor consistency between the enhanced samples and the original
samples in the current data enhancement methods. In this paper, we propose a data …

The proposed transfer learning-based fault diagnosis models have achieved good results in
multi-source domain generalization (MDG) tasks. However, research on single-source …

The diagnosis of mechanical faults under unknown operating conditions has been
extensively investigated. In real industrial scenarios, fault diagnosis often faces challenges …

Abstract Network Architecture Search (NAS) automates hyperparameter adjustments in
deep learning models, offering a potent solution for building intelligent fault diagnosis …

In engineering, imbalanced data collected from planet bearings causes most intelligent
models to shrink the decision boundary of minor classes and degrade diagnostic accuracy …

In recent years, domain generalization fault diagnosis methods have effectively addressed
the challenges of bearing fault diagnosis under unseen working conditions. Most existing …