Temporal Knowledge Graph Informer Network for Remaining Useful Life Prediction
Yuanming Zhang, Weiyue Zhou, Jiacheng Huang, Xiaohang Jin, Gang Xiao
Abstract
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.