Application of multi-layer denoising based on ensemble empirical mode decomposition in extraction of fault feature of rotating machinery.

Aiming at the problem that the weak features of non-stationary vibration signals are difficult to extract under strong background noise, a multi-layer noise reduction method based on ensemble empirical mode decomposition (EEMD) is proposed. First, the original vibration signal is decomposed by EEMD,...

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Autores principales:	Kangping Gao, Xinxin Xu, Jiabo Li, Shengjie Jiao, Ning Shi
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3e4af52dca9347dba7454e4d586e988d
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spelling	oai:doaj.org-article:3e4af52dca9347dba7454e4d586e988d2021-12-02T20:06:49ZApplication of multi-layer denoising based on ensemble empirical mode decomposition in extraction of fault feature of rotating machinery.1932-620310.1371/journal.pone.0254747https://doaj.org/article/3e4af52dca9347dba7454e4d586e988d2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254747https://doaj.org/toc/1932-6203Aiming at the problem that the weak features of non-stationary vibration signals are difficult to extract under strong background noise, a multi-layer noise reduction method based on ensemble empirical mode decomposition (EEMD) is proposed. First, the original vibration signal is decomposed by EEMD, and the main intrinsic modal components (IMF) are selected using comprehensive evaluation indicators; the second layer of filtering uses wavelet threshold denoising (WTD) to process the main IMF components. Finally, the virtual noise channel is introduced, and FastICA is used to de-noise and unmix the IMF components processed by the WTD. Next, perform spectral analysis on the separated useful signals to highlight the fault frequency. The feasibility of the proposed method is verified by simulation, and it is applied to the extraction of weak signals of faulty bearings and worn polycrystalline diamond compact bits. The analysis of vibration signals shows that this method can efficiently extract weak fault characteristic information of rotating machinery.Kangping GaoXinxin XuJiabo LiShengjie JiaoNing ShiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0254747 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kangping Gao Xinxin Xu Jiabo Li Shengjie Jiao Ning Shi Application of multi-layer denoising based on ensemble empirical mode decomposition in extraction of fault feature of rotating machinery.
description	Aiming at the problem that the weak features of non-stationary vibration signals are difficult to extract under strong background noise, a multi-layer noise reduction method based on ensemble empirical mode decomposition (EEMD) is proposed. First, the original vibration signal is decomposed by EEMD, and the main intrinsic modal components (IMF) are selected using comprehensive evaluation indicators; the second layer of filtering uses wavelet threshold denoising (WTD) to process the main IMF components. Finally, the virtual noise channel is introduced, and FastICA is used to de-noise and unmix the IMF components processed by the WTD. Next, perform spectral analysis on the separated useful signals to highlight the fault frequency. The feasibility of the proposed method is verified by simulation, and it is applied to the extraction of weak signals of faulty bearings and worn polycrystalline diamond compact bits. The analysis of vibration signals shows that this method can efficiently extract weak fault characteristic information of rotating machinery.
format	article
author	Kangping Gao Xinxin Xu Jiabo Li Shengjie Jiao Ning Shi
author_facet	Kangping Gao Xinxin Xu Jiabo Li Shengjie Jiao Ning Shi
author_sort	Kangping Gao
title	Application of multi-layer denoising based on ensemble empirical mode decomposition in extraction of fault feature of rotating machinery.
title_short	Application of multi-layer denoising based on ensemble empirical mode decomposition in extraction of fault feature of rotating machinery.
title_full	Application of multi-layer denoising based on ensemble empirical mode decomposition in extraction of fault feature of rotating machinery.
title_fullStr	Application of multi-layer denoising based on ensemble empirical mode decomposition in extraction of fault feature of rotating machinery.
title_full_unstemmed	Application of multi-layer denoising based on ensemble empirical mode decomposition in extraction of fault feature of rotating machinery.
title_sort	application of multi-layer denoising based on ensemble empirical mode decomposition in extraction of fault feature of rotating machinery.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/3e4af52dca9347dba7454e4d586e988d
work_keys_str_mv	AT kangpinggao applicationofmultilayerdenoisingbasedonensembleempiricalmodedecompositioninextractionoffaultfeatureofrotatingmachinery AT xinxinxu applicationofmultilayerdenoisingbasedonensembleempiricalmodedecompositioninextractionoffaultfeatureofrotatingmachinery AT jiaboli applicationofmultilayerdenoisingbasedonensembleempiricalmodedecompositioninextractionoffaultfeatureofrotatingmachinery AT shengjiejiao applicationofmultilayerdenoisingbasedonensembleempiricalmodedecompositioninextractionoffaultfeatureofrotatingmachinery AT ningshi applicationofmultilayerdenoisingbasedonensembleempiricalmodedecompositioninextractionoffaultfeatureofrotatingmachinery
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Application of multi-layer denoising based on ensemble empirical mode decomposition in extraction of fault feature of rotating machinery.

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