Recent advancements in empirical wavelet transform and its applications

Abstract

Empirical wavelets transform (EWT) is a fully adaptive signal-analysis approach, which is similar to the empirical mode decomposition (EMD) but has a consolidated mathematical theory, and is appealing in designing automatic algorithm for a variety of signal processing tasks. EWT first estimates the frequency components presented in the given signal, then, computes the boundaries and extracts the oscillatory components based on the computed boundaries. Because of the excellent performance of the EWT in decomposing the nonlinear and non-stationary signals, it has been successfully applied into a number of problems. The last six years have seen the development of EWT. This paper presents a general overview of the recent advancements made in research on the EWT algorithm and its state-of-the-art applications in a wide range of areas, such as machine fault diagnosis, seismic data analysis, image processing, power system monitoring, and medical disease diagnosis, which aims at providing some comprehensive references for reader concerning with EWT. We place emphasis on the applications of using such signal-analysis algorithm throughout with illustrative examples. Finally, the potential avenues for the future trends and directions associated with EWT are discussed.