Preload-Loss State Identification of Bolted Joints Using Multi-Sensor Electromechanical Impedance Signals and a Distance-Weighted Graph Convolutional Network

To address the insufficient fusion of electromechanical impedance (EMI) response features from multiple sensors and the limited characterization of spatial relationships between sensors and bolt nodes in four-bolt connection structures, this study proposes an improved graph convolutional network (GCN) model integrating Batch Normalization (BN) and Distance Weighting (DW) strategies for bolt preload-loss state identification. First, PZT sensor nodes and bolt nodes are jointly represented as a graph structure, and the correlation coefficient deviation (CCD) is extracted as the EMI response feature. Then, a weighted adjacency matrix is constructed according to the geometric distances between sensor nodes and bolt nodes to describe the spatial coupling relationships among different nodes. Subsequently, the weighted adjacency matrix and node features are input into the GCN, and a BN layer is introduced after the graph convolutional layers to reduce the influence of multi-channel feature distribution variations on model training stability. Experimental results on a four-bolt connection structure show that the proposed GCN-BN-DW model outperforms the Basic GCN, GCN-BN, GCN-DW, and several benchmark models in terms of prediction accuracy and stability. Under the strict five-fold cross-validation protocol, the proposed model achieves a test MAE of 2.400±0.100, RMSE of 3.302±0.239, MASE of 0.300±0.013, and R2 of 0.821±0.033. These results indicate that the proposed model can effectively integrate multi-sensor EMI features and sensor–bolt spatial relationships, providing a feasible graph-based modeling approach for bolt preload-loss state identification.