Leveraging graph attention networks for enhanced latent defect detection in precision built-in spindle assembly lines

Abstract

This study introduces a novel approach employing Graph Attention Networks (GAT) to detect pre-installation defects in built-in spindles. Traditional quality control relies heavily on manual inspections and basic mechanical testing, which often miss subtle defects. Using vibration datasets from 13 spindles with identical specifications, this research applies a GAT-based diagnostic method, transforming vibration signals into graph representations. GAT's attention mechanism effectively extracts essential node and structural features, enabling early and accurate defect identification. Experimental results demonstrate that the proposed GAT model significantly outperforms conventional techniques such as k-nearest neighbors (KNN), Support Vector Machines (SVM), and Graph Convolutional Networks (GCN). By adding noise to simulate harsh operational conditions, the GAT model maintained superior clustering and classification performance, achieving 100% accuracy under noiseless conditions and exhibiting exceptional robustness even at a challenging -5 dB signal-to-noise ratio (SNR). Additionally, GAT effectively handles imbalanced datasets and displays strong generalization capabilities, underscoring its practical industrial potential. This research marks a notable advancement in spindle manufacturing quality control, highlighting promising future directions for deep learning in industrial diagnostics.