Dual-Channel Degradation Monitoring Based on Graph Neural Network for Aero-Engine Remaining Useful Life Prediction

Abstract

For aircraft engine predictive maintenance (PdM) programs, accurate remaining useful life (RUL) predictions can significantly reduce unscheduled maintenance downtime and ensure engine safety. To this end, we introduce a novel dual-channel degradation monitoring (DCDM) algorithm designed to minimize RUL prediction errors. Unlike traditional RUL prediction algorithms based on graph neural networks (GNNs), the proposed DCDM model extracts fault features from both node embeddings and changes in graph structures. This dual-channel approach allows for the fusion of fault information, improving the model’s ability to extract features across different fault patterns for RUL prediction. During the graph structure learning (GSL) process, domain-specific knowledge and a dynamic graph learning algorithm are integrated to generate graphs, enhancing the interpretability of graph representation. In addition, by introducing node sparse encoding as a model input, the DCDM model’s capability to discern critical features is significantly improved. The predictive performance of the DCDM model and the effectiveness of its individual components are validated using the C-MAPSS dataset. The results demonstrate the superior accuracy of the proposed method compared to existing approaches.