Convolution-Graph Attention Network With Sensor Embeddings for Remaining Useful Life Prediction of Turbofan Engines

The remaining useful life (RUL) prediction of turbofan engines is beneficial to safe operation and maintenance. Turbofan engines generally require multisensors to monitor the operational state, but current mainstream RUL prediction models tend to ignore the spatial correlations between sensors. Although few research has used graph neural networks (GNNs) to capture such correlations, there are some limitations in the way the graph structure is determined. To this end, we introduce sensor embeddings and propose a novel RUL prediction model based on a convolution-graph attention network (ConvGAT). First, the time-series of every sensor is segmented using a sliding time window and the data within that window is taken as the model input. Next, every sensor is treated as a node in the graph. The features of sensor data, which serve as the initial features of the corresponding sensor node, are extracted using a convolutional layer. Then, a learnable embedding vector is specially introduced for every sensor and the spatial correlations between sensors are captured using a graph attention network (GAT). Particularly, the sensor embeddings play an important role in the graph structure learning as well as the graph attention mechanism. Finally, the sensor node features output by the GAT are fused with the corresponding sensor embeddings and passed through a fully connected layer to predict the RUL of turbofan engines. The ConvGAT-based RUL prediction model's performance is evaluated using a benchmark dataset regarding turbofan engines, i.e., the C-MAPSS dataset. The experimental results indicate the superior performance of the proposed model. Our code is available at https://github.com/CUG-FDGroup.