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.

The simulation signals generated by the bearing dynamics model have a big gap with the actual signals, which limits their efficacy in bearing fault diagnosis. Therefore, it is valuable to build an accurate digital twin model of faulty rolling bearing . Firstly, a multi-degree-of-freedom bearing fault dynamics model is constructed in the virtual space for generating the vibration responses of bearing parts. Then considering that the frequency spectrum contains more characteristic information than the time-domain signal, a frequency-domain bi-directional long short-term memory (Bi-LSTM) cycle generative adversarial network (CycleGAN) named FBC-GAN is proposed to construct the frequency-domain coupling mapping relationship between the multipart vibration responses and the measured signals. In the proposed network, Bi-LSTM is used for enhancing the feature extraction ability. Meantime, a new spectrum-constraint loss is proposed to ensure the frequency-domain mapping. Next, the simulated fault bearing signals close to the actual signals are generated by FBC-GAN and Fourier transform . Finally, the results of two experiments show the superiority of the proposed method over other advanced data augmentation methods in bearing fault diagnosis with the imbalanced samples.