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The research aims to improve the prediction of the Remaining Useful Life (RUL) of rotating machinery by utilizing a multi-graph feature fusion model.

March 15, 2026Applied SciencesOpen Access

Remaining Useful Life Prediction for Rotating Machinery via Multi-Graph-Based Spatiotemporal Feature Fusion

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The research aims to improve the prediction of the Remaining Useful Life (RUL) of rotating machinery by utilizing a multi-graph feature fusion model.
Developed a multi-graph structured model for RUL prediction.
Constructed two correlation graphs: a prior correlation graph and a similarity correlation graph.
Employed a Graph Attention Network (GAT) for aggregating spatial features.
Used Long Short-Term Memory (LSTM) to extract temporal features from operational data.
The model effectively enhanced RUL prediction performance.
Simulation on C-MAPSS dataset validated the robustness of the proposed method.

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Simulation experiments demonstrate improved RUL prediction in rotating machinery, suggesting a novel multi-graph model approach.

Cao et al. (2026) studied this question.

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