Adaptive Signal Decomposition and Reconstruction for Bridge Structural Dynamic Testing

In order to extract structural information from the bridge structural dynamic signal contaminated with high noise level, a novel adaptive decomposition and reconstruction method is proposed by combining the Ensemble Empirical Mode Decomposition (EEMD) and Principal Component Analysis (PCA) for the specific characteristics of bridge structural dynamic signals. Based on the in-depth analysis of mode mixing in empirical mode decomposition, the uniformity of probability density function for white noise is adopted to improve the mode mixing pattern I, and the correlation analysis is used to ameliorate the mode mixing pattern II, then the calculation efficiency and decomposition accuracy are upgraded greatly in the improved EEMD. The effectiveness of the proposed method is verified by both of the analog and testing signals from real bridge structure. The verified results showed that the proposed method can decompose and denoise effectively the bridge dynamic signal contaminated with high noise, and can extract accurately the structural information from the testing signal, furthermore it is applicable to the dynamic testing analysis of real bridge structure.