Graph Neural Networks

Reliable gearbox condition monitoring requires methods that can process high-frequency vibration measurements while retaining information about sensor locations and drivetrain structure. We investigate a two-stage framework that combines sparse filtering with graph neural networks for wind turbine gearbox fault detection. Sparse filtering learns compact features from vibration segments, and a knowledge-based graph associates these features with gearbox components, sensors, and health-state entities. A basic graph neural network and a gated graph neural network produce a single health prediction, whereas a gated graph sequence neural network generates structured sequences that associate a predicted state with component and sensor information. Experiments use eight accelerometer channels from the National Renewable Energy Laboratory gearbox condition-monitoring benchmark. With 100 learned features, the sparse-filtering gated graph neural network achieves a reported classification accuracy of 93.74%, compared with 89.28% for its manually engineered feature counterpart. The sequence model achieves reported accuracies of 90.73% and 90.36% in the one- and two-fault-signal cases, respectively. These results support the use of learned vibration representations within a structured sensor graph. The evidence is restricted to the evaluated benchmark and operating condition; independent component localization, transfer across turbines, and deployment latency require further validation.

Reconstructing multi-human body meshes from a single monocular image is a challenging problem, requiring both individual body meshes and relative 3D positions to produce a coherent scene representation. We introduce MUG (Multi-hUman Graph network), which uses a single graph neural network to construct coherent multi-human meshes from only 2D poses. Our approach demonstrates that pose-based methods can jointly perform depth reasoning and multi-human mesh generation. Existing image-based methods often rely on lab-collected datasets with accurate 3D labels, introducing domain gaps when applied to in-the-wild or art images due to differences in appearance and context. In contrast, MUG leverages the geometric consistency of 2D poses across diverse datasets to mitigate such discrepancies. MUG operates in three phases. First, it constructs a novel heterogeneous graph from multi-human 2D poses, connecting nodes across people and within individuals to capture inter-human interactions and body geometry, including skeletal and mesh structures. Second, a dual-branch graph neural network predicts inter-human depth relations and root-joint-relative mesh coordinates. Finally, these outputs are combined to construct complete multi-human 3D meshes. Despite using only 2D poses and a streamlined architecture, MUG outperforms existing multi-human mesh estimation methods across datasets from diverse domains, including RH, MuPoTS-3D, and 3DPW. Qualitative results show that MUG can handle art images, through-wall scenarios, and poor lighting conditions when paired with advanced 2D pose networks. Both qualitative and quantitative evaluations highlight its generalization ability in open-world scenarios.