An Improved Empirical Wavelet Transform for Noisy and Non-Stationary Signal Processing

Ping Liao,Cuifang Zhuang

doi:10.1109/access.2020.2968851

Ping Liao, Cuifang Zhuang

Open Access

https://doi.org/10.1109/access.2020.2968851

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Journal: IEEE Access	Publication Date: Jan 1, 2020
Citations: 13	License type: CC BY 4.0

Affiliation: Central South University

Abstract

Empirical wavelet transform (EWT) has become an effective tool for signal processing. However, its sensitivity to noise may bring side effects on the analysis of some noisy and non-stationary signals, especially for the signal which contains the close frequency components. In this paper, an improved empirical wavelet transform is proposed. This method combines the advantages of piecewise cubic Hermite interpolating polynomial (PCHIP) and the EWT, and is named PCHIP-EWT. The main idea of the proposed method is to select useful sub-bands from the spectrum envelope. The proposed method selects the maximum points of the spectrum to reconstruct the spectrum envelope on the basis of PCHIP. Then, a new concept and a threshold named the Local Power (LP) and λ are defined. Based on the new concept LP and the λ, the useful sub-bands can be obtained. Finally, the experimental results demonstrate that the PCHIP-EWT is effective in analyzing noise and non-stationary signals, especially those that contain the closely-spaced frequencies.

Highlights

Most real systems are worked in the complex dynamic environment, while the dynamic response of these complicated mechanisms are high nonlinear, which brings many difficulties to the signal feature extraction
Considering the above methods fail to decompose closely spaced frequency components in the TF plane and reducing the number of parameters that need to be determined in advance, in this work, we present an improved empirical wavelet transform named piecewise cubic Hermite interpolating polynomial (PCHIP)-Empirical wavelet transform (EWT) that detects boundaries in the spectral envelope calculated by PCHIP algorithm
In this paper, a new method called the PCHIP-EWT was proposed for the decomposition of the noisy and non-stationary signals

Summary

Introduction

Most real systems are worked in the complex dynamic environment, while the dynamic response of these complicated mechanisms are high nonlinear, which brings many difficulties to the signal feature extraction To analyze these types of signals, wavelet transformation (WT) is proposed. It attempts to decompose the processed signals into a set of intrinsic modes and separate the dominant or interesting features from other irrelevant modes by some criteria. This approach has been proved to be very effective for analyzing noisy and non-stationary signals.

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Results

Conclusion