Spatio-Temporal Propagation: An Extended Message Passing Graph Neural Network for Remaining Useful Life Prediction

The deep learning (DL) based method for predicting remaining useful life (RUL) has gained lots of attention in the industrial equipment sector. Due to the complexity of modern industrial equipment and the necessity of monitoring multivariate time-series data to obtain comprehensive health information, DL models with spatio-temporal feature extraction have been developed to achieve accurate RUL prediction results. However, in the three typical approaches for spatio-temporal feature extraction (parallel, sequential, and nested), each module is independent and separate, making them ineffective in fusing spatio-temporal information at the same time. Most existing approaches use separated modules to extract spatial and temporal features, where convolution, graph, and recurrent neural networks are often applied. To overcome these limitations, this paper introduces a unified paradigm for spatio-temporal feature fusion. By extending the message-passing graph neural network (GNN), the spatio-temporal propagation (STP) model is constructed. Using GNN as a single structure, the model can simultaneously match information propagation at both spatial and temporal scales. STP allows a flexible and intuitive way to construct RUL prediction models. An implementation of STP-GNN with an attention mechanism is given and discussed, and a case study on RUL prediction of turbofan engines is reported. Experimental results verify the effectiveness of STP-GNN, and highlight its interpretability in the process of RUL prediciton.