Block-Wise Sliding Recursive Wavelet Transform and Its Application in Real-Time Vehicle-Induced Signal Separation

Vehicle-induced response separation is a crucial issue in structural health monitoring (SHM). This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements of monitoring data. To extend the separation target from a fixed dataset to a continuously updating data stream, a block-wise sliding framework is first developed. This framework is further optimized considering the characteristics of real-time data streams, and its advantage in computational efficiency is theoretically demonstrated. During the decomposition and reconstruction processes, information from neighboring data blocks is fully utilized to reduce algorithmic complexity. In addition, a delay-setting strategy is introduced for each processing window to mitigate boundary effects, thereby balancing accuracy and efficiency. Simulated signal experiments are conducted to determine the optimal delay configuration and to verify the algorithm’s superior performance, achieving a lower Root Mean Square Error (RMSE) and only 0.0249 times the average computational time compared with the original algorithm. Furthermore, strain signals from the Lieshi River Bridge are employed to validate the method. The proposed algorithm successfully separates the static trend from vehicle-induced responses in real time across different sampling frequencies, demonstrating its effectiveness and applicability in real-time bridge monitoring.