An Adaptive Filtering Method for Bridge Vibration Signals Based on Improved CEEMDAN and Multi-Scale Permutation Entropy

Abstract

Aiming at the serious noise of bridge vibration signals in complex environment, this paper proposed an adaptive filtering and denoising optimization method for bridge structural health monitoring. The method took CEEMDAN algorithm as the core, during the step-by-step decomposition of original signals, white noise with opposite signs was added in each stage, meanwhile multi-scale permutation entropy (MPE) was introduced to analyze the mean entropy of each intrinsic mode function (IMF) at different scales, and components with serious noise were eliminated to complete the first filtering; then, in order to optimize the remaining IMFs for signal reconstruction and ensuring the smoothness and similarity of filtering, an optimized reconstruction model was established to complete the second filtering. Compared with the CEEMDAN method, the proposed method could solve the problems of mode mixing and endpoint effect with good completeness, orthogonality, and signal-to-noise ratio. At last, the advantages and application value of the proposed method were verified again by the vibration signal analysis of a real long-span bridge structure.

Application of permutation entropy and factorization network in the classification of congenital heart disease

“…EEMD (Li et al, 2018) is to add Gaussian white noise with uniform frequency distribution on the basis of EMD, so that the heart sound signal has continuity feature at different scales, thereby reducing the influence of mode aliasing. CEEMDAN (Hu et al, 2018) calculates the only residual signal by adding adaptive Gaussian white noise in each decomposition stage. To set S(t) be the original signal, Z i t ð Þ be the jth mode component obtained by EMD decomposition, n i t ð Þ be the zero mean value added at the ith time, the white noise sequence with variance of 1, and ε be the signal-to-noise ratio control coefficient.…”