Remaining useful life prediction of aeroengine based on Parallel-GraphSAGE-GRU
GUO Wenwen
WANG Guohu
YUAN Yongliang
WANG Peiying
GUO Yinxiao
Abstract：[Objective]Deep learning-based remaining useful life(RUL)prediction of aeroengine provides a foundation for guaranteeing the reliability of the aeroengine.However,most researches are devoted to the acquisition of degenerate information in Euclidean space,while topological relations in non-Euclidean space can provide additional degenerate information.Thus,a novel RUL prediction method named parallel graph node embedding learning-gated recurrent unit(Parallel-GraphSAGE-GRU)was proposed in this work.[Methods]Firstly,the maximum mutual information method was used to describe the relationships among monitoring parameters of aeroengine.Then,a series of graph inputs was constructed using a fixed time window to obtain topological relations in the non-Euclidean space of the aeroengine.Besides,GRU was used for the acquisition the temporal degradation relations of the monitoring parameters for aeroengine in the Euclidean space.The implicit vectors of GraphSAGE and GRU at each moment were concatenated and mapped to the RUL values of the input degradation information by fully connected neural network.The C-MAPSS dataset was used to validate the effectiveness and advancement of the proposed method,and the best R2 of the proposed method reached 0.988 6.[Results]The results indicate that this method is feasible in ensuring prediction accuracy and computational efficiency.
Keywords：EngineRemaining useful lifeFeature information extractionGraph node embedding learningGated recurrent unit
Publication Date：2026-07-31
Online Publishing Date：2026-09-12(First online date of this platform, not the publication date of the document)
Pages：8( 32-39 )