A Compound fault diagnosis for rolling bearings method based on blind source separation and ensemble empirical mode decomposition.

A Compound fault signal usually contains multiple characteristic signals and strong confusion noise, which makes it difficult to separate week fault signals from them through conventional ways, such as FFT-based envelope detection, wavelet transform or empirical mode decomposition individually. In o...

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Autores principales:	Huaqing Wang, Ruitong Li, Gang Tang, Hongfang Yuan, Qingliang Zhao, Xi Cao
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2a547c267fa24519856fd8ca78faa2a8
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spelling	oai:doaj.org-article:2a547c267fa24519856fd8ca78faa2a82021-11-25T05:57:45ZA Compound fault diagnosis for rolling bearings method based on blind source separation and ensemble empirical mode decomposition.1932-620310.1371/journal.pone.0109166https://doaj.org/article/2a547c267fa24519856fd8ca78faa2a82014-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0109166https://doaj.org/toc/1932-6203A Compound fault signal usually contains multiple characteristic signals and strong confusion noise, which makes it difficult to separate week fault signals from them through conventional ways, such as FFT-based envelope detection, wavelet transform or empirical mode decomposition individually. In order to improve the compound faults diagnose of rolling bearings via signals' separation, the present paper proposes a new method to identify compound faults from measured mixed-signals, which is based on ensemble empirical mode decomposition (EEMD) method and independent component analysis (ICA) technique. With the approach, a vibration signal is firstly decomposed into intrinsic mode functions (IMF) by EEMD method to obtain multichannel signals. Then, according to a cross correlation criterion, the corresponding IMF is selected as the input matrix of ICA. Finally, the compound faults can be separated effectively by executing ICA method, which makes the fault features more easily extracted and more clearly identified. Experimental results validate the effectiveness of the proposed method in compound fault separating, which works not only for the outer race defect, but also for the rollers defect and the unbalance fault of the experimental system.Huaqing WangRuitong LiGang TangHongfang YuanQingliang ZhaoXi CaoPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 10, p e109166 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Huaqing Wang Ruitong Li Gang Tang Hongfang Yuan Qingliang Zhao Xi Cao A Compound fault diagnosis for rolling bearings method based on blind source separation and ensemble empirical mode decomposition.
description	A Compound fault signal usually contains multiple characteristic signals and strong confusion noise, which makes it difficult to separate week fault signals from them through conventional ways, such as FFT-based envelope detection, wavelet transform or empirical mode decomposition individually. In order to improve the compound faults diagnose of rolling bearings via signals' separation, the present paper proposes a new method to identify compound faults from measured mixed-signals, which is based on ensemble empirical mode decomposition (EEMD) method and independent component analysis (ICA) technique. With the approach, a vibration signal is firstly decomposed into intrinsic mode functions (IMF) by EEMD method to obtain multichannel signals. Then, according to a cross correlation criterion, the corresponding IMF is selected as the input matrix of ICA. Finally, the compound faults can be separated effectively by executing ICA method, which makes the fault features more easily extracted and more clearly identified. Experimental results validate the effectiveness of the proposed method in compound fault separating, which works not only for the outer race defect, but also for the rollers defect and the unbalance fault of the experimental system.
format	article
author	Huaqing Wang Ruitong Li Gang Tang Hongfang Yuan Qingliang Zhao Xi Cao
author_facet	Huaqing Wang Ruitong Li Gang Tang Hongfang Yuan Qingliang Zhao Xi Cao
author_sort	Huaqing Wang
title	A Compound fault diagnosis for rolling bearings method based on blind source separation and ensemble empirical mode decomposition.
title_short	A Compound fault diagnosis for rolling bearings method based on blind source separation and ensemble empirical mode decomposition.
title_full	A Compound fault diagnosis for rolling bearings method based on blind source separation and ensemble empirical mode decomposition.
title_fullStr	A Compound fault diagnosis for rolling bearings method based on blind source separation and ensemble empirical mode decomposition.
title_full_unstemmed	A Compound fault diagnosis for rolling bearings method based on blind source separation and ensemble empirical mode decomposition.
title_sort	compound fault diagnosis for rolling bearings method based on blind source separation and ensemble empirical mode decomposition.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/2a547c267fa24519856fd8ca78faa2a8
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A Compound fault diagnosis for rolling bearings method based on blind source separation and ensemble empirical mode decomposition.

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