A graph neural network-based bearing fault detection method

Nature·Mar 31

✦ NabkaNews BriefAuto-summarized from multiple outlets · verify with the source

Researchers are exploring various methods for bearing fault detection using graph neural networks. Different approaches have been proposed, including the use of knowledge graphs, dual-branch spatio-temporal graph networks, and adaptive feature fusion networks. These methods aim to improve fault diagnosis in industrial systems, including robotics and wind-powered heating systems.

Full coverage

KG-EIGNN: a knowledge graph based graph neural network for fault detection in industrial systems

Nature·May 19

Dual-branch spatio-temporal graph network for bearing fault diagnosis

Nature·Mar 11

Adaptive feature fusion network for machine fault diagnosis with multiple knowledge based graphs

Nature·Apr 21

Bearing fault diagnosis based on multi-branch enhanced GhostNet with adaptive focal loss

Nature·Apr 21

Intelligent fault diagnosis based on multi-source information fusion and attention-enhanced networks

Nature·Oct 16

Intelligent fault prediction and diagnosis for wind-powered heating systems using graph neural networks

Nature·Nov 7

Real time fault diagnosis in industrial robotics using discrete and slantlet wavelet transformations

Nature·Oct 1

Bearing fault detection by using graph autoencoder and ensemble learning | Scientific Reports

Nature·Mar 3

View original report at Nature ↗

Fetching the latest headlines…