A semi-supervised fault diagnosis method based on dynamic decay learning strategy and hypergraph attention network

To solve the fault diagnosis difficulties in autonomous underwater vehicle (AUV) thrusters, a semi-supervised AUV fault diagnosis method based on dynamic decay learning strategy and hypergraph attention network (HGAN) is proposed. Firstly, an attention mechanism is introduced into hypergraph convolutional networks (HGCN) to construct HGAN. Then, the HGAN and graph convolutional network (GCN) are designed in parallel architecture to capture both the dynamic and static features of the input graph signal simultaneously. Finally, a dynamic decay learning strategy is introduced, which improves the training efficiency of the proposed model. The diagnostic precision of the proposed approach is verified through experiment analysis. Besides, its superiority over the other related methods is also verified through comparison study.