Temporal Knowledge Graph Informer Network for Remaining Useful Life Prediction

Remaining useful life (RUL) prediction is of great significance to ensure the safety and reliability of equipment. Graph neural network-based methods show great potential to improve RUL prediction performance by extracting spatiotemporal features from sensor monitoring data. However, current methods construct sensor-based homogeneous graphs without considering equipment component structure data and prior knowledge, which cannot characterize the dependency between sensors and studied equipment accurately. To solve this problem, we propose a temporal knowledge graph (TKG) informer network for RUL prediction. A TKG of equipment health status integrates sensor data with structure data through prior knowledge, so as to characterize various spatiotemporal features accurately. The graph structure and node information (spatial-domain features) of the TKG at each moment is embedded in a low-dimensional temporal graph representation (TGR). An informer network extracts variable information (temporal-domain features) to generate TGR for RUL prediction. The proposed method was evaluated on public datasets and was found to achieve much higher performance than other state-of-the-art models. The TKG datasets are available at IEEE DataPort: https://dx.doi.org/10.21227/jgs2-kt12.