An improved GNN using dynamic graph embedding mechanism: A novel end-to-end framework for rolling bearing fault diagnosis under variable working conditions
Zidong Yu, Changhe Zhang, Chao Deng
Topics & Concepts
Computer scienceFault (geology)GraphEmbeddingConvolutional neural networkBearing (navigation)Pattern recognition (psychology)Artificial intelligenceArtificial neural networkControl theory (sociology)Theoretical computer scienceSeismologyControl (management)GeologyMachine Fault Diagnosis TechniquesGear and Bearing Dynamics AnalysisOccupational Health and Safety Research