Federated learning (FL) enables large amounts of participants to construct a global learning
model, while storing training data privately at each client device. A fundamental issue in this …

Federated learning (FL) refers to the learning paradigm that trains machine learning models
directly in the decentralized systems consisting of smart edge devices without transmitting …

In real industry, due to changes in operating conditions and differences in systems of
interest, domain shift is a common problem, which results in the degradation of the …

With the rapid development of artificial intelligence (AI) in recent years, fault diagnostics for
industrial applications have leaped toward partially or fully automatic provided by the …

For imbalanced bearing fault diagnosis, generative adversarial networks (GANs) are a
common data augmentation (DA) approach. Nevertheless, current GAN-based methods …

Anomalous data are such data that deviate from a large number of normal data points, which
often have negative impacts on various systems. Current anomaly detection technology …

Intelligent fault diagnosis of bearings has been a heated research topic in the prognosis and
health management of rotary machinery systems, due to the increasing amount of available …

Rolling bearing fault diagnosis with imbalanced data is a challenging task. It is a significant
means to augment the data into balanced datasets. A novel data augmentation method …

Deep-learning-based intelligent diagnosis is a popular method to ensure the safe operation
of rolling bearings. However, practical diagnostic tasks are often subject to a lack of labeled …

Deep learning has redefined state-of-the-art performances in the research of intelligent fault
diagnosis, however, most studies assumed that the training and testing data have the same …