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A Novel Adaptive Signal Processing Method Based on Enhanced Empirical Wavelet Transform Technology

Empirical wavelet transform (EWT) is a novel adaptive signal decomposition method, whose main shortcoming is the fact that Fourier segmentation is strongly dependent on the local maxima of the amplitudes of the Fourier spectrum. An enhanced empirical wavelet transform (MSCEWT) based on maximum-minim...

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Autores principales: Zhao, Huimin, Zuo, Shaoyan, Hou, Ming, Liu, Wei, Yu, Ling, Yang, Xinhua, Deng, Wu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6210451/
https://www.ncbi.nlm.nih.gov/pubmed/30282951
http://dx.doi.org/10.3390/s18103323
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author Zhao, Huimin
Zuo, Shaoyan
Hou, Ming
Liu, Wei
Yu, Ling
Yang, Xinhua
Deng, Wu
author_facet Zhao, Huimin
Zuo, Shaoyan
Hou, Ming
Liu, Wei
Yu, Ling
Yang, Xinhua
Deng, Wu
author_sort Zhao, Huimin
collection PubMed
description Empirical wavelet transform (EWT) is a novel adaptive signal decomposition method, whose main shortcoming is the fact that Fourier segmentation is strongly dependent on the local maxima of the amplitudes of the Fourier spectrum. An enhanced empirical wavelet transform (MSCEWT) based on maximum-minimum length curve method is proposed to realize fault diagnosis of motor bearings. The maximum-minimum length curve method transforms the original vibration signal spectrum to scale space in order to obtain a set of minimum length curves, and find the maximum length curve value in the set of the minimum length curve values for obtaining the number of the spectrum decomposition intervals. The MSCEWT method is used to decompose the vibration signal into a series of intrinsic mode functions (IMFs), which are processed by Hilbert transform. Then the frequency of each component is extracted by power spectrum and compared with the theoretical value of motor bearing fault feature frequency in order to determine and obtain fault diagnosis result. In order to verify the effectiveness of the MSCEWT method for fault diagnosis, the actual motor bearing vibration signals are selected and the empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) methods are selected for comparative analysis in here. The results show that the maximum-minimum length curve method can enhance EWT method and the MSCEWT method can solve the shortcomings of the Fourier spectrum segmentation and can effectively decompose the bearing vibration signal for obtaining less number of intrinsic mode function (IMF) components than the EMD and EEMD methods. It can effectively extract the fault feature frequency of the motor bearing and realize fault diagnosis. Therefore, the study provides a new method for fault diagnosis of rotating machinery.
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spelling pubmed-62104512018-11-02 A Novel Adaptive Signal Processing Method Based on Enhanced Empirical Wavelet Transform Technology Zhao, Huimin Zuo, Shaoyan Hou, Ming Liu, Wei Yu, Ling Yang, Xinhua Deng, Wu Sensors (Basel) Article Empirical wavelet transform (EWT) is a novel adaptive signal decomposition method, whose main shortcoming is the fact that Fourier segmentation is strongly dependent on the local maxima of the amplitudes of the Fourier spectrum. An enhanced empirical wavelet transform (MSCEWT) based on maximum-minimum length curve method is proposed to realize fault diagnosis of motor bearings. The maximum-minimum length curve method transforms the original vibration signal spectrum to scale space in order to obtain a set of minimum length curves, and find the maximum length curve value in the set of the minimum length curve values for obtaining the number of the spectrum decomposition intervals. The MSCEWT method is used to decompose the vibration signal into a series of intrinsic mode functions (IMFs), which are processed by Hilbert transform. Then the frequency of each component is extracted by power spectrum and compared with the theoretical value of motor bearing fault feature frequency in order to determine and obtain fault diagnosis result. In order to verify the effectiveness of the MSCEWT method for fault diagnosis, the actual motor bearing vibration signals are selected and the empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) methods are selected for comparative analysis in here. The results show that the maximum-minimum length curve method can enhance EWT method and the MSCEWT method can solve the shortcomings of the Fourier spectrum segmentation and can effectively decompose the bearing vibration signal for obtaining less number of intrinsic mode function (IMF) components than the EMD and EEMD methods. It can effectively extract the fault feature frequency of the motor bearing and realize fault diagnosis. Therefore, the study provides a new method for fault diagnosis of rotating machinery. MDPI 2018-10-03 /pmc/articles/PMC6210451/ /pubmed/30282951 http://dx.doi.org/10.3390/s18103323 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhao, Huimin
Zuo, Shaoyan
Hou, Ming
Liu, Wei
Yu, Ling
Yang, Xinhua
Deng, Wu
A Novel Adaptive Signal Processing Method Based on Enhanced Empirical Wavelet Transform Technology
title A Novel Adaptive Signal Processing Method Based on Enhanced Empirical Wavelet Transform Technology
title_full A Novel Adaptive Signal Processing Method Based on Enhanced Empirical Wavelet Transform Technology
title_fullStr A Novel Adaptive Signal Processing Method Based on Enhanced Empirical Wavelet Transform Technology
title_full_unstemmed A Novel Adaptive Signal Processing Method Based on Enhanced Empirical Wavelet Transform Technology
title_short A Novel Adaptive Signal Processing Method Based on Enhanced Empirical Wavelet Transform Technology
title_sort novel adaptive signal processing method based on enhanced empirical wavelet transform technology
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6210451/
https://www.ncbi.nlm.nih.gov/pubmed/30282951
http://dx.doi.org/10.3390/s18103323
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