Deformation Feature Extraction and Analysis Based on Improved Variational Mode Decomposition

Deformation data feature extraction and analysis is a key issue to be solved in building deformation prediction, early warning and mechanism interpretation. For this reason, An improved variational mode decomposition algorithm(IVMD)is established to construct a new method of feature extraction and analysis of deformation data. The K value of variational mode decomposition is determined by sample entropy, center frequency ratio and correlation coefficient to establish improved variational mode decomposition, and the new algorithm is tested by simulation signal and bridge deformation monitoring data. The results show that IVMD can accurately extract the component signals contained in the simulation signals, and its correlation coefficient (R) and root mean square error(RMSE) indexes are superior to empirical mode decomposition (EMD) and wavelet transform methods. The reliability of IVMD algorithm is verified theoretically. Based on IVMD, the natural frequency and damping characteristic parameters of the bridge can be extracted accurately, and the deformation characteristic information of the bridge caused by temperature change, multi-path effect and other environmental impacts can be extracted well, which confirms the validity of IVMD in feature extraction and analysis of deformation monitoring data.