Deep-Learning-Enabled Data Synchronization for Noninteger Time Lags in Wireless Structural Health Monitoring

Accurate synchronization of distributed sensor measurements is essential for reliable vibration-based structural health monitoring (SHM). Wireless Internet of Things (IoT) systems, while attractive for large-scale SHM deployments, inevitably introduce unknown time lags arising from clock drift and communication delays. Conventional frequency-domain (FD) estimators and feature-based machine-learning (ML) models struggle to handle such asynchronous measurements, especially when delays are smaller than the sampling interval. To address these limitations, this paper proposed a learning-based synchronization framework that integrates a time-lag convolutional neural network with attention (TLCA) to directly estimate continuous-valued time lags from raw vibration signals, together with a resampling-based alignment strategy to correct non-integer delays. The proposed framework was validated using both simulated structural responses and field data from a wireless SHM system deployed on the Jindo Bridge. By directly learning time relationships from raw vibration signal pairs, TLCA achieves sub-millisecond accuracy and consistently outperforms conventional frequency-domain and feature-based learning methods, with about an order-of-magnitude improvement in precision. It also maintained strong robustness under increased noise and modal coupling. With efficient data-level alignment and low computational complexity of the overall framework, it can enable accurate and real-time synchronization for practical IoT-based SHM applications.