Entropy-graph attention network for semi-supervised fault diagnosis of rotating machinery under extremely low label rate and variable rotating speed conditions

Under extremely low label rate, the existing graph neural networks ( GNN ) suffer from insufficient mining of inter-node association information during graph construction. In industrial production, rotating machinery often operates under variable rotating speed conditions, and labeling fault samples is costly. Here, aiming at the above 2 problems, an entropy-graph attention network was proposed based on Jenson-Shannon (JS) relative entropy and dynamic graph attention network ( DGAT ), and it was applied in semi-supervised fault diagnosis of rotating machinery under extremely low label rate and variable rotating speed conditions. Firstly, a graph construction method based on JS relative entropy was designed to fully explore the correlation information among samples in GNN. Secondly, a semi-supervised learning model based on the entropy-DGAN was constructed to further explore fault sensitive features in samples with dynamic attention mechanism. Finally, the proposed method was verified on bearing and gearbox datasets under variable rotating speed conditions, and the results showed that the proposed method can correctly diagnose different fault types in rotating machinery under extremely low label rate of no more than 1%; its performance is superior to other commonly used GNNS. © 2024 Chinese Vibration Engineering Society. All rights reserved.