A Novel Spiking Graph Attention Network for Intelligent Fault Diagnosis of Planetary Gearboxes

As an essential component in the power transmission system of rotating machinery, planetary gearboxes are often subjected to extreme operating conditions, including heavy loads and low speeds, which can cause key components to fail over time. Early planetary gearbox fault features are weak, coupled with the impact of strong noise in the industrial field, which brings great challenges to the accurate identification and diagnosis of planetary gearbox faults. To address these issues, we propose a novel spiking graph attention network (Spiking-GAT) for intelligent fault diagnosis of planetary gearboxes, which realizes synchronous extraction of temporal and spatial features of the original signal, to accurately identify the fault category and severity of planetary gearbox. First, a graph data construction method based on the chaos theory is proposed. A multiphase coupled chaotic oscillator array based on Duffing (MP-COAD) is established to reconstruct the original signal, and the K-nearest neighbor (KNN) classifier is used to construct the reconstructed signal as graph data. Second, a novel Spiking-GAT intelligent fault diagnosis framework has been developed, which provides an adaptive spiking coding approach for graph data and deep mining and extraction of spatial–temporal features to accurately identify the health status of planetary gearboxes. Finally, the experimental and industrial fault simulation test rigs of the planetary gearbox are designed, and three cases are studied to verify the accuracy and stability of Spiking-GAT. The results show that the classification accuracy of Spiking-GAT reaches 100% on the datasets of the two test rigs and has excellent noise robustness.