Deep learning for time-series prediction in IIoT: progress, challenges, and prospects
Time-series prediction plays a crucial role in the Industrial Internet of Things (IIoT) to enable
intelligent process control, analysis, and management, such as complex equipment …
intelligent process control, analysis, and management, such as complex equipment …
Transfer learning for prognostics and health management: Advances, challenges, and opportunities
As failure data is usually scarce in practice upon preventive maintenance strategy in
prognostics and health management (PHM) domain, transfer learning provides a …
prognostics and health management (PHM) domain, transfer learning provides a …
Fedsr: A simple and effective domain generalization method for federated learning
Federated Learning (FL) refers to the decentralized and privacy-preserving machine
learning framework in which multiple clients collaborate (with the help of a central server) to …
learning framework in which multiple clients collaborate (with the help of a central server) to …
A domain feature decoupling network for rotating machinery fault diagnosis under unseen operating conditions
T Gao, J Yang, W Wang, X Fan - Reliability Engineering & System Safety, 2024 - Elsevier
Operating conditions reflect the mission evolution of rotating machinery in specific
application scenarios. The monitoring data under different operating conditions exhibit …
application scenarios. The monitoring data under different operating conditions exhibit …
Domain fuzzy generalization networks for semi-supervised intelligent fault diagnosis under unseen working conditions
In recent years, domain adaptation methods have made remarkable achievements in fault
diagnosis under variable working conditions. However, the methods usually fail when target …
diagnosis under variable working conditions. However, the methods usually fail when target …
Mutual-assistance semisupervised domain generalization network for intelligent fault diagnosis under unseen working conditions
Generalizing deep models to unseen working conditions is an essential topic for intelligent
fault diagnosis. Existing domain generalization-based fault diagnosis (DGFD) methods …
fault diagnosis. Existing domain generalization-based fault diagnosis (DGFD) methods …
Adaptive class center generalization network: A sparse domain-regressive framework for bearing fault diagnosis under unknown working conditions
Fault diagnosis is essential to ensure the bearing safety in smart manufacturing. As the
rotating bearings usually work under variable working conditions, there may exist …
rotating bearings usually work under variable working conditions, there may exist …
Multi-scale style generative and adversarial contrastive networks for single domain generalization fault diagnosis
Abstract Domain generalization methods can effectively identify machinery faults under
unseen new target working conditions. Nevertheless, most of them rely on data from multiple …
unseen new target working conditions. Nevertheless, most of them rely on data from multiple …
Domain generalization for cross-domain fault diagnosis: An application-oriented perspective and a benchmark study
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 …
identically distributed data of training and testing. However, domain shift between the …
Adaptive open set domain generalization network: Learning to diagnose unknown faults under unknown working conditions
Recently, domain generalization techniques have been introduced to enhance the
generalization capacity of fault diagnostic models under unknown working conditions. Most …
generalization capacity of fault diagnostic models under unknown working conditions. Most …