RGCNU: Recurrent Graph Convolutional Network With Uncertainty Estimation for Remaining Useful Life Prediction
Qiwu Zhu, Qingyu Xiong, Zhengyi Yang, Yu Yang
Abstract
Dear Editor, This letter focuses on the problem of remaining useful life (RUL) prediction of equipment. Existing graph neural network (GCN)-based approaches merely provide the point estimation of RUL. However, the estimated RUL often varies widely due to the model parameters and the noise in data. It is important to know the uncertainty in predictions for reliable risk analysis and maintenance decision making. To map the relationship between noisy condition monitoring data and RUL with uncertainty, we propose a recurrent graph convolutional network with uncertainty estimation (RGCNU) for RUL prediction. In our approach, the correlation exploiting module captures the spatial-temporal correlations based on the learned graph structure. Furthermore, the fusion module associates the RUL prediction and data uncertainty to improve the robustness of the model to noisy data.