A fault feature extraction algorithm based on CEEMD-TVD-MOMEDA

Rolling bearings are indispensable key components in mechanical equipment, and they are also one of the most easily damaged components. To solve the problem of bearing fault feature extraction under strong noise interference, a combination of complementary ensemble average empirical mode decomposit...

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Autores principales:	Jingzong Yang, Tianqing Yang, Chunchao Shi
Formato:	article
Lenguaje:	EN
Publicado:	Tamkang University Press 2021
Materias:	complementary ensemble average empirical mode decomposition (ceemd) total variation denoising (tvd) multipoint optimal minimum entropy deconvolution adjusted fault feature extraction Engineering (General). Civil engineering (General) TA1-2040 Chemical engineering TP155-156 Physics QC1-999
Acceso en línea:	https://doaj.org/article/fa15a986bad5459a8d6206ee871d9d29
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id	oai:doaj.org-article:fa15a986bad5459a8d6206ee871d9d29
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spelling	oai:doaj.org-article:fa15a986bad5459a8d6206ee871d9d292021-11-23T12:53:01ZA fault feature extraction algorithm based on CEEMD-TVD-MOMEDA10.6180/jase.202202_25(1).00082708-99672708-9975https://doaj.org/article/fa15a986bad5459a8d6206ee871d9d292021-11-01T00:00:00Zhttp://jase.tku.edu.tw/articles/jase-202202-25-1-0008https://doaj.org/toc/2708-9967https://doaj.org/toc/2708-9975Rolling bearings are indispensable key components in mechanical equipment, and they are also one of the most easily damaged components. To solve the problem of bearing fault feature extraction under strong noise interference, a combination of complementary ensemble average empirical mode decomposition (CEEMD), total variation denoising (TVD), and multipoint optimal minimum entropy deconvolution adjusted(MOMEDA) is proposed. Firstly, decompose the vibration signal into several signal components. Secondly, the qualified IMF signal components are selected by combining with the cross-correlation analysis criteria for reconstruction, and TVD is used to reduce the noise of the signal. Thirdly, MOMEDA is used to filter the denoised signal, so as to enhance the periodic impact component. Finally, the envelope spectrum of the filtered signal is analyzed. The effectiveness of the proposed method is verified by the simulation signals and the bearing fault data set of Case Western Reserve University. The experimental results show that the proposed method can not only reduce the noise interference but also effectively extract and identify the rolling bearing fault features. Compared with the results obtained by traditional LMD and ITD methods, it has a better recognition effect.Jingzong YangTianqing YangChunchao ShiTamkang University Pressarticlecomplementary ensemble average empirical mode decomposition (ceemd)total variation denoising (tvd)multipoint optimal minimum entropy deconvolution adjustedfault feature extractionEngineering (General). Civil engineering (General)TA1-2040Chemical engineeringTP155-156PhysicsQC1-999ENJournal of Applied Science and Engineering, Vol 25, Iss 1, Pp 71-83 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	complementary ensemble average empirical mode decomposition (ceemd) total variation denoising (tvd) multipoint optimal minimum entropy deconvolution adjusted fault feature extraction Engineering (General). Civil engineering (General) TA1-2040 Chemical engineering TP155-156 Physics QC1-999
spellingShingle	complementary ensemble average empirical mode decomposition (ceemd) total variation denoising (tvd) multipoint optimal minimum entropy deconvolution adjusted fault feature extraction Engineering (General). Civil engineering (General) TA1-2040 Chemical engineering TP155-156 Physics QC1-999 Jingzong Yang Tianqing Yang Chunchao Shi A fault feature extraction algorithm based on CEEMD-TVD-MOMEDA
description	Rolling bearings are indispensable key components in mechanical equipment, and they are also one of the most easily damaged components. To solve the problem of bearing fault feature extraction under strong noise interference, a combination of complementary ensemble average empirical mode decomposition (CEEMD), total variation denoising (TVD), and multipoint optimal minimum entropy deconvolution adjusted(MOMEDA) is proposed. Firstly, decompose the vibration signal into several signal components. Secondly, the qualified IMF signal components are selected by combining with the cross-correlation analysis criteria for reconstruction, and TVD is used to reduce the noise of the signal. Thirdly, MOMEDA is used to filter the denoised signal, so as to enhance the periodic impact component. Finally, the envelope spectrum of the filtered signal is analyzed. The effectiveness of the proposed method is verified by the simulation signals and the bearing fault data set of Case Western Reserve University. The experimental results show that the proposed method can not only reduce the noise interference but also effectively extract and identify the rolling bearing fault features. Compared with the results obtained by traditional LMD and ITD methods, it has a better recognition effect.
format	article
author	Jingzong Yang Tianqing Yang Chunchao Shi
author_facet	Jingzong Yang Tianqing Yang Chunchao Shi
author_sort	Jingzong Yang
title	A fault feature extraction algorithm based on CEEMD-TVD-MOMEDA
title_short	A fault feature extraction algorithm based on CEEMD-TVD-MOMEDA
title_full	A fault feature extraction algorithm based on CEEMD-TVD-MOMEDA
title_fullStr	A fault feature extraction algorithm based on CEEMD-TVD-MOMEDA
title_full_unstemmed	A fault feature extraction algorithm based on CEEMD-TVD-MOMEDA
title_sort	fault feature extraction algorithm based on ceemd-tvd-momeda
publisher	Tamkang University Press
publishDate	2021
url	https://doaj.org/article/fa15a986bad5459a8d6206ee871d9d29
work_keys_str_mv	AT jingzongyang afaultfeatureextractionalgorithmbasedonceemdtvdmomeda AT tianqingyang afaultfeatureextractionalgorithmbasedonceemdtvdmomeda AT chunchaoshi afaultfeatureextractionalgorithmbasedonceemdtvdmomeda AT jingzongyang faultfeatureextractionalgorithmbasedonceemdtvdmomeda AT tianqingyang faultfeatureextractionalgorithmbasedonceemdtvdmomeda AT chunchaoshi faultfeatureextractionalgorithmbasedonceemdtvdmomeda
_version_	1718416741994856448

A fault feature extraction algorithm based on CEEMD-TVD-MOMEDA

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