Abstract
The adaptive decomposition algorithm is a powerful tool for signal analysis, because it can decompose signals into several narrow-band components, which is advantageous to quantitatively evaluate signal characteristics. In this paper, we present a comparative study of four kinds of adaptive decomposition algorithms, including some algorithms deriving from empirical mode decomposition (EMD), empirical wavelet transform (EWT), variational mode decomposition (VMD) and Vold–Kalman filter order tracking (VKF_OT). Their principles, advantages and disadvantages, and improvements and applications to signal analyses in dynamic analysis of mechanical system and machinery fault diagnosis are showed. Examples are provided to illustrate important influence performance factors and improvements of these algorithms. Finally, we summarize applicable scopes, inapplicable scopes and some further works of these methods in respect of precise filters and rough filters. It is hoped that the paper can provide a valuable reference for application and improvement of these methods in signal processing.
Highlights
At present, a great number of scholars conduct investigations about adaptive decomposition algorithms
Some important improvements have been done for EMD in some other algorithms such as complementary ensemble empirical mode decomposition (CEEMD), complementary ensemble empirical mode decomposition with adaptive noises (CEEMDAN) and improved complementary ensemble empirical mode decomposition with adaptive noises, which are more competent at processing non-linear and non-stationary signals
In the improved version of CEEMDAN, the operation M(·) is denoted as the operator, which produces the local mean of the upper envelope and the lower envelope, and the operation Ek(·) is defined which generates the kth mode obtained by EMD, and ωi is denoted as the white noise with
Summary
A great number of scholars conduct investigations about adaptive decomposition algorithms. Empirical mode decomposition (EMD), empirical wavelet transform (EWT), variational mode decomposition (VMD) and Vold–Kalman filter order tracking (VKF_OT) are popular adaptive decomposition algorithms. These methods show excellent capacity of processing non-linear and non-stationary signals. Some important improvements have been done for EMD in some other algorithms such as complementary ensemble empirical mode decomposition (CEEMD), complementary ensemble empirical mode decomposition with adaptive noises (CEEMDAN) and improved complementary ensemble empirical mode decomposition with adaptive noises (improved CEEMDAN), which are more competent at processing non-linear and non-stationary signals.
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