Motor current signal analysis using hypergraph neural networks for fault diagnosis of electromechanical system

• The raw current signal is processed using time-shifting to avoid power-line interference. • The hypergraph structure applicable to current time-series data is established. • The complex interrelationship between nodes is represented by hyperedges. • Framework for current signal fault diagnosis of electromechanically coupled equipment is established. • Several different sets of rotating mechanical equipment experiments are designed to verify the superiority as well as the robustness of TS-HGNN in current signal fault diagnosis. Graph based networks are becoming an emerging trend in the field of fault diagnosis because of their powerful ability to mine the interrelationships between nodes. However, the existing graph-based networks are limited to mining the association relationship between adjacent nodes, which cannot reflect the strong association relationship between multiple nodes and thus affect the graph data quality. To solve these problems, a time-shifting based hypergraph neural network (TS-HGNN) is proposed for the accurate classification of fault types in electromechanical coupled systems. First, the time shifting method is applied to pre-process the original current signal to remove the power-line interference. Then, a hypergraph structure applicable to current signal is established to form complex interrelationships and a hyperedge convolution operation is designed to obtain the interrelationships of higher-order data for representation learning. Finally, several datasets are designed to verify the superiority and robustness of TS-HGNN in current signal fault classification.