Remaining useful life prediction based on spatiotemporal autoencoder

Remaining Useful Life (RUL) prediction has received a lot of attention as the core of prognostics and health management (PHM) technology. Deep learning-based RUL prediction methods are currently the most popular, and in order to solve the problem that most of the current deep RUL prediction studies do not consider the structural information between sensors, we propose a spatiotemporal autoencoder (STAE)-based RUL prediction method. The method extracts the time domain information from the data through the temporal convolutional network. It obtains the structural information of the sensors by converting the time series data into a graph structure by utilizing the maximal information coefficient and then performing the graph representation learning. For the two obtained features, a feature fusion method based on the graph attention mechanism is used for fusion and finally, the new fused features are utilized for RUL prediction. To validate the effectiveness of STAE, we conducted experiments on the simulated dataset C-MAPSS and the real satellite dataset SCS-PSS, and our proposed method outperforms the baseline method on both datasets. The results suggest that considering structural information between sensors in the deep RUL prediction model can improve prediction accuracy.