With the coming of the industrial Big Data era, data-driven fault diagnosis models emerge
recently and show potential results in many studies. However, it is impractical to collect …

Intelligent data-driven machinery fault diagnosis methods have been successfully and
popularly developed in the past years. While promising diagnostic performance has been …

In cloud manufacturing systems, fault diagnosis is essential for ensuring stable
manufacturing processes. The most crucial performance indicators of fault diagnosis models …

Recently, deep neural network (DNN) models work incredibly well, and edge computing has
achieved great success in real-world scenarios, such as fault diagnosis for large-scale …

Federated learning is a deep learning optimization method that can solve user privacy
leakage, and it has positive significance in applying industrial equipment fault diagnosis …

In the past year, various deep learning-based fault diagnosis methods have been designed
to guarantee the stable, safe, and efficient operation of electromechanical systems. To …

Recently, a considerable literature has grown up around the few-sample fault diagnosis
task, in which few samples of fault data are available for model training. The lack of fault …

Data-driven-based intelligent fault diagnosis (IFD) approaches have been broadly
developed. In actual industry, not all data of mechanical equipment are accessible …

In recent years, artificial intelligent techniques have been extensively explored in the field of
health monitoring and fault diagnosis due to their powerful capabilities. In this paper, we …

Despite rapid advances in machine learning based fault diagnosis, their identical
distribution assumption of the training (source domain) and testing data (target domain) is …