Multi-Target domains adaptive network based on transfer graph feature alignment for rotating machinery fault diagnosis

Abstract Multi-target domain adaptive (MTDA) methods, which take into account the variability of working condition and identify faults in different target domains more accurately, are becoming a growing focus of fault diagnosis. However, the neglect of structural information within the key data, the robustness impact of the disturbing information strongly correlated with operating conditions, and the inconsistencies in matching the source and multi-target domains (SMTD) are the limitations of MTDA. Therefore, a transfer graph feature alignment multi-target domains adaptive network (TGAMDN) is proposed to reduce the disturbing information and extract structural information. Firstly, the data structure between the SMTD is modeled through the construction of a new transfer graph sample generation module (GSG), with the data structure information being learned by a shared graph neural network. Secondly, a novel weighting mechanism and the corresponding training framework are constructed to effectively reduce the impact of interfering information and the boundary difference between different data classes is enhanced using the fitted circle method. Finally, the weighted hybrid alignment strategies are used to minimize differences between domains and resolve inconsistent matches. The performance of the TGAMDN is validated using a rotating machinery dataset across various transfer tasks under different rotational speed and load conditions.