Comprehensive Dynamic Structure Graph Neural Network for Aero-Engine Remaining Useful Life Prediction

Accurate remaining useful life (RUL) prediction is of great significance to the safe operation of aircraft. Graph neural network (GNN) can describe the spatial structure relationship between variables, which provides a new solution to the problem of RUL prediction. In this paper, we propose a new RUL prediction method based on GNN, which is named Comprehensive Dynamic Structure Graph Neural Network (CDSG). The proposed CDSG not only fully exploits the health information hidden in the condition monitoring data, but also takes into account the structural characteristics of the aero-engine. This method utilizes the designed dynamic graph learning (DGL) module to capture the potential dynamic relationships between time series data, and combines them with the structural characteristics of the aero-engine to generate the comprehensive graph structure. In addition, the global temporal features obtained by visual graph (VG) algorithm and graph convolutional network (GCN) are introduced into the proposed model, which can improve the predictive performance of the method. The validity of CDSG is verified with the C-MAPSS and N-CMAPSS datasets. The experimental results show that the proposed CDSG has better performance compared to the existing state-of-the-art methods.