Dynamic curvature pooling graph convolutional network to fuse multi-sensor signals for remaining useful life prediction

The core objective of graph neural network (GNN)-based remaining useful life (RUL) prediction methods for equipment with multi-source sensors is to learn effective graph representations, and graph pooling is an efficient approach to achieve it. However, existing graph pooling techniques are limited in modeling hierarchical structures and have limitations in embedding space representation. To overcome these limitations, a dynamic curvature pooling graph convolutional network (DCPGCN) is proposed for RUL prediction of equipment with multi-source sensors. DCPGCN develops a hyperbolic hierarchical graph pooling framework. By leveraging the geometric advantages of hyperbolic space for hierarchical representation, the proposed framework more effectively captures multi-level structural information in graphs, significantly improving the overall structural fidelity of the graph representation. Moreover, a curvature predictor driven by pooling path deviation is proposed. By quantifying the geometric distortion along leaf-to-root paths in hyperbolic space, the predictor dynamically adjusts the curvature parameter, improving the embedding space’s adaptability and expressiveness for the graph’s hierarchical structure. Finally, experiments on the CMAPSS dataset demonstrate that the proposed method outperforms multiple state-of-the-art approaches in prediction accuracy, while experiments on real-world wind turbine RUL prediction further confirm its superiority and potential in engineering applications.