Data decomposition method combining permutation entropy and spectral substitution with ensemble empirical mode decomposition

Adaptive Stochastic Resonance Method Based on Quantum Genetic Algorithm and its Application in Dynamic Characteristic Identification of Bridge GNSS Monitoring Data

“…Focusing on the global optimization ability of quantum genetic algorithm, thereby improving the accuracy and prominent detection of frequency and its subtle changes, the QGA-ASR method does not consider the computational complexity reducing like the discussion in [111], thus it is as time-consuming as GA -ASR. This method also does not consider the bridge dynamic characteristic identification and signal denoising described in [94] and [112], which can be conducted in a situation where the noise amplitude is not large and the approximate frequency is unknown. However, in analyzing the bridge health monitoring data, QGA-ASR has a better ability than GA-ASR to identify or examine the frequency in the normal operation stage of the bridge.…”

“…Compared with difficulties in simulating and estimating the displacement error in accelerometer monitoring, a certain rule exists to address for the noise in 3D coordinate time series of bridge monitoring deformation from GNSS [84][85][86][87]. A growing number of researchers have adopted time series analysis methods to extract the dynamic deformation feature information on bridges through GNSS monitoring [23,24,30,72,[88][89][90][91][92][93][94]. The existing methods show excellent performance in the vibration feature extraction of bridge monitoring data, but the weak vibration feature signal is seriously polluted by noise in coordinate time series.…”

Review of Bridge Structural Health Monitoring Based on GNSS: From Displacement Monitoring to Dynamic Characteristic Identification

“…A series of results has been obtained. Huang and Wang et al [296] proposed a data decomposition method that combines EMD, permutation entropy (PE) and spectral substitution, which can effectively weaken the noise and retain the original signal when processing noisy bridge GNSS monitoring data sequences. With this method, they obtained clearer dynamic characteristics of the Wuhan Baishazhou Yangtze River Bridge.…”

Analysis of Vehicle Platform Vibration Based on Empirical Mode Decomposition

“…Zhou proposed a method to eliminate the end effect and false mode of EMD [8]. Based on ensemble empirical mode decomposition (EEMD), complementary ensemble empirical mode decomposition (CEEMD), and partial ensemble empirical mode decomposition (PEEMD), Huang developed a new method that combines permutation entropy and spectral substitution with ensemble EMD to solve the problem of mode mixing [9]. Zheng proposed the PEEMD method to resolve the mode mixing problem [10].…”