An Improved Empirical Mode Decomposition Method for Vibration Signal

Abstract

With the development of electronic measurement and signal processing technology, nonstationary and nonlinear signal characteristics are widely used in the fields of error diagnosis, system recognition, and biomedical instruments. Whether these features can be extracted effectively usually affects the performance of the entire system. Based on the above background, the research purpose of this paper is an improved vibration empirical mode decomposition method. This article introduces a method of blasting vibration signal processing—Differential Empirical Mode Decomposition (DEMD), combined with phosphate rock engineering blasting vibration monitoring test, and Empirical Mode Decomposition (EMD) to compare and analyze the frequency screening of blasting vibration signals, the aliasing distortion, and the power spectrum characteristics of the decomposed signal. The results show that compared with EMD, DEMD effectively suppresses signal aliasing and distortion, and from the characteristics of signal power spectrum changes, DEMD extracts different dominant frequency components, and the frequency screening effect of blasting vibration signals is superior to EMD. It can bring about an obvious improvement in accuracy, and the calculation time is about 4 times that of the EMD method. Based on the ground analysis of ground motion signals, this paper uses the EMD algorithm to analyze measured ground blast motion signals and study its velocity characteristics and differential time, which provides a new way of studying motion signals.

Ocean Turbulence Denoising and Analysis Using a Novel EMD-Based Denoising Method

“…First, the measured raw turbulence signal S(t) is decomposed into a finite number n of intrinsic mode functions (IMFs) that represents different time scales and frequency from high to low using the EMD method [24]:…”

Identification of the blasting vibration characteristics of groundwater-sealed tunnel

“…The ensemble EMD (EEMD) and complementary EEMD (CEEMD) methods are improved based on EMD by adding Gaussian white noise. These methods divide the original signal into components of different scales in the time–frequency space, are all adaptive, noise-assisted data analysis methods 16 , can solve the problem of modal aliasing to a certain extent and realise adaptive decomposition and time–frequency feature extraction of non-stationary signals 17 . The CEEMD with adaptive noise (CEEMDAN), which is also improved on the basis of EMD by adaptively adding white noise, reduces the phenomenon of modal aliasing, overcomes the problem of reconstruction error and can accurately reconstruct the original signal 18 .…”