Multivariate Nonlinear Sparse Mode Decomposition and Its Application in Gear Fault Diagnosis

Multi-channel signal has more abundant and accurate state characteristic information than single channel signal. How to separate fault characteristic information from the multi-channel signal is the key of fault diagnosis. As two typical multi-channel signal decomposition methods, multivariate empir...

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Autores principales:	Haiyang Pan, Wanwan Jiang, Qingyun Liu, Jinde Zheng
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Multivariate nonlinear sparse mode decomposition singular local linear operator gear fault diagnosis Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/1789dd9e615f4b749521c74e800dc5b3
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spelling	oai:doaj.org-article:1789dd9e615f4b749521c74e800dc5b32021-11-24T00:00:49ZMultivariate Nonlinear Sparse Mode Decomposition and Its Application in Gear Fault Diagnosis2169-353610.1109/ACCESS.2021.3127495https://doaj.org/article/1789dd9e615f4b749521c74e800dc5b32021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9612194/https://doaj.org/toc/2169-3536Multi-channel signal has more abundant and accurate state characteristic information than single channel signal. How to separate fault characteristic information from the multi-channel signal is the key of fault diagnosis. As two typical multi-channel signal decomposition methods, multivariate empirical mode decomposition (MEMD) and multivariate variational mode decomposition (MVMD) are widely used in multi-channel signal analysis. However, MEMD and MVMD use cyclic iteration to complete the analysis of multi-channel signals, and it is difficult to overcome their inherent defects. In view of this, based on nonlinear sparse mode decomposition (NSMD), this paper proposes a multivariate nonlinear sparse mode decomposition (MNSMD) by constraining singular local linear operators to separate the natural oscillation modes in multi-channel signal. By constraining singular local linear operators into signal decomposition, MNSMD has obvious advantages in restraining mode aliasing and robustness. In addition, the local narrow-band component is used as the basis function for iteration, and the component signal is obtained by approaching the original signal. Through the simulation signal and gear fault signal analysis, the results show that, compared with MEMD and MVMD methods, MNSMD method can effectively complete gear fault diagnosis.Haiyang PanWanwan JiangQingyun LiuJinde ZhengIEEEarticleMultivariate nonlinear sparse mode decompositionsingular local linear operatorgearfault diagnosisElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 154265-154274 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Multivariate nonlinear sparse mode decomposition singular local linear operator gear fault diagnosis Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Multivariate nonlinear sparse mode decomposition singular local linear operator gear fault diagnosis Electrical engineering. Electronics. Nuclear engineering TK1-9971 Haiyang Pan Wanwan Jiang Qingyun Liu Jinde Zheng Multivariate Nonlinear Sparse Mode Decomposition and Its Application in Gear Fault Diagnosis
description	Multi-channel signal has more abundant and accurate state characteristic information than single channel signal. How to separate fault characteristic information from the multi-channel signal is the key of fault diagnosis. As two typical multi-channel signal decomposition methods, multivariate empirical mode decomposition (MEMD) and multivariate variational mode decomposition (MVMD) are widely used in multi-channel signal analysis. However, MEMD and MVMD use cyclic iteration to complete the analysis of multi-channel signals, and it is difficult to overcome their inherent defects. In view of this, based on nonlinear sparse mode decomposition (NSMD), this paper proposes a multivariate nonlinear sparse mode decomposition (MNSMD) by constraining singular local linear operators to separate the natural oscillation modes in multi-channel signal. By constraining singular local linear operators into signal decomposition, MNSMD has obvious advantages in restraining mode aliasing and robustness. In addition, the local narrow-band component is used as the basis function for iteration, and the component signal is obtained by approaching the original signal. Through the simulation signal and gear fault signal analysis, the results show that, compared with MEMD and MVMD methods, MNSMD method can effectively complete gear fault diagnosis.
format	article
author	Haiyang Pan Wanwan Jiang Qingyun Liu Jinde Zheng
author_facet	Haiyang Pan Wanwan Jiang Qingyun Liu Jinde Zheng
author_sort	Haiyang Pan
title	Multivariate Nonlinear Sparse Mode Decomposition and Its Application in Gear Fault Diagnosis
title_short	Multivariate Nonlinear Sparse Mode Decomposition and Its Application in Gear Fault Diagnosis
title_full	Multivariate Nonlinear Sparse Mode Decomposition and Its Application in Gear Fault Diagnosis
title_fullStr	Multivariate Nonlinear Sparse Mode Decomposition and Its Application in Gear Fault Diagnosis
title_full_unstemmed	Multivariate Nonlinear Sparse Mode Decomposition and Its Application in Gear Fault Diagnosis
title_sort	multivariate nonlinear sparse mode decomposition and its application in gear fault diagnosis
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/1789dd9e615f4b749521c74e800dc5b3
work_keys_str_mv	AT haiyangpan multivariatenonlinearsparsemodedecompositionanditsapplicationingearfaultdiagnosis AT wanwanjiang multivariatenonlinearsparsemodedecompositionanditsapplicationingearfaultdiagnosis AT qingyunliu multivariatenonlinearsparsemodedecompositionanditsapplicationingearfaultdiagnosis AT jindezheng multivariatenonlinearsparsemodedecompositionanditsapplicationingearfaultdiagnosis
_version_	1718416080008904704

Multivariate Nonlinear Sparse Mode Decomposition and Its Application in Gear Fault Diagnosis

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