Impact time history reconstruction using dynamic weighted graph neural network under sensor fault situations

Abstract

This article introduces a dynamic weighted graph neural network (DWGNN) for precise reconstruction of impact force time histories under both fault and nonfault conditions in sensor networks. The DWGNN model integrates a one-dimensional convolutional neural network for extracting features from sensor signals and a graph neural network that dynamically adjusts its weight matrix based on node feature similarity, thereby reducing the impact of faulty sensor signals. Incorporating physical constraints, such as the distances between impact and sensor locations, improves the model’s performance. This innovative approach enables the accurate reconstruction of impact forces using only nonfaulty training data. Experimental validation on a stiffened metal plate, with mixed artificially generated and real measured signals, demonstrates the DWGNN model’s high accuracy and robustness. Data augmentation with fault signals significantly enhances the model’s accuracy under challenging fault conditions, proving the DWGNNs potential for real-time structural health monitoring and fault diagnosis in complex engineering structures.