Heterogeneous Knowledge Graph Inference-Assisted Aeroengine Rotor Skidding Tracing and Regulation

The multifactor coupling influence on the skidding behavior of aeroengine rotors presents significant challenges in locating the skidding causes and developing effective skidding suppression measures. However, ongoing research into fault mechanism and knowledge graph (KG) facilitates the accurate tracing of complex faults. We propose a heterogeneous KG inference-assisted skidding tracing and regulation strategy for aeroengine rotor. First, a skidding heterogeneous KG is constructed based on the text and data knowledge, in which the skidding level classification rules are determined for the first time. Second, we design an adaptive distributed metalearning algorithm to extract data features by combining the structural characteristics of the skidding KG. Third, few-shot knowledge inference is performed using the relation-metalearning graph convolutional network. Finally, we develop skidding suppression measures by tracing the input knowledge under unknown working states, enabling effective regulation of skidding behavior.