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.INDEX TERMS Empirical wavelet transform, machine health monitoring, seismic data analysis, signal and image processing, power system signals analysis, medical disease diagnosis.

Adaptive Separation of Respiratory and Heartbeat Signals among Multiple People Based on Empirical Wavelet Transform Using UWB Radar

“…To address these limitations, in this paper, we propose a novel vital sign separation method based on empirical wavelet transform (EWT) using SFCW-UWB radar. EWT is an adaptive signal analysis approach that is suitable for dealing with nonlinearity in the model and non-stationary signals with elegant mathematical theory [ 14 ]. In recent years, EWT has been successfully applied to medical disease diagnosis, image processing, machine fault diagnosis, and seismic data analysis [ 14 ].…”

“…EWT is an adaptive signal analysis approach that is suitable for dealing with nonlinearity in the model and non-stationary signals with elegant mathematical theory [ 14 ]. In recent years, EWT has been successfully applied to medical disease diagnosis, image processing, machine fault diagnosis, and seismic data analysis [ 14 ]. In our work, prior information of the relatively stable frequency ranges of respiration and heartbeat signals is adopted to derive the EWT boundaries, which are then used to separate respiration and heartbeat signals without the need to employ other referenced signals.…”

Data Driven Method for Event Classification via Regional Segmentation of Power Systems

“…Wavelet analysis is used as an efficient tool to analyze the nonstationary signal such as PMU signal owing to the use of time-localized basis [12], [28]. The key point of nonstationary signal analysis is the localization in time-frequency domain to capture the time-varying frequency component and useful description of the signals.…”

A Novel Fault Diagnosis Approach for Rolling Bearing Based on High-Order Synchrosqueezing Transform and Detrended Fluctuation Analysis

“…In fact, bearing is prone to failure due to long-term impact and overload [1]- [4]. Once a fault occurs in rolling bearing, it will lead to expensive production shutdowns in manufacturing industry [5]. Therefore, machine condition monitoring is of great importance to ensure the safe and efficient operation of equipment.…”