Weighted Reconstruction and Improved Eigenclass Combination Method for the Detection of Bearing Faults

Aiming at the difficulty of extracting and classifying early bearing faults, a fault diagnosis method based on weighted average time-varying filtering empirical mode decomposition and improved eigenclass is proposed in this paper. Firstly, the bearing fault signal is decomposed into a series of intr...

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Autores principales:	Zhengyu Du, Jie Ma, Chao Ma, Min Huang, Weiwei Sun
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/23fa626fe2d5494581a170587b119874
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spelling	oai:doaj.org-article:23fa626fe2d5494581a170587b1198742021-11-22T01:11:14ZWeighted Reconstruction and Improved Eigenclass Combination Method for the Detection of Bearing Faults1563-514710.1155/2021/5503107https://doaj.org/article/23fa626fe2d5494581a170587b1198742021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/5503107https://doaj.org/toc/1563-5147Aiming at the difficulty of extracting and classifying early bearing faults, a fault diagnosis method based on weighted average time-varying filtering empirical mode decomposition and improved eigenclass is proposed in this paper. Firstly, the bearing fault signal is decomposed into a series of intrinsic mode functions by the signal decomposition method, and the amplitude of the component is modulated by the weighted average method to enhance the fault impulse component. Then, the fractional Fourier transform is used to filter the reconstructed signal. Regarding classification issues, the eigenclass classifier is optimized by the IDE method that can be used for feature dimensionality reduction. Finally, the optimal features are selected and input into the IDE-EigenClass model. The experimental results show that the bearing fault diagnosis method proposed in this paper has higher accuracy and stability than the traditional PNN, SVM, BP, and other methods.Zhengyu DuJie MaChao MaMin HuangWeiwei SunHindawi LimitedarticleEngineering (General). Civil engineering (General)TA1-2040MathematicsQA1-939ENMathematical Problems in Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
spellingShingle	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939 Zhengyu Du Jie Ma Chao Ma Min Huang Weiwei Sun Weighted Reconstruction and Improved Eigenclass Combination Method for the Detection of Bearing Faults
description	Aiming at the difficulty of extracting and classifying early bearing faults, a fault diagnosis method based on weighted average time-varying filtering empirical mode decomposition and improved eigenclass is proposed in this paper. Firstly, the bearing fault signal is decomposed into a series of intrinsic mode functions by the signal decomposition method, and the amplitude of the component is modulated by the weighted average method to enhance the fault impulse component. Then, the fractional Fourier transform is used to filter the reconstructed signal. Regarding classification issues, the eigenclass classifier is optimized by the IDE method that can be used for feature dimensionality reduction. Finally, the optimal features are selected and input into the IDE-EigenClass model. The experimental results show that the bearing fault diagnosis method proposed in this paper has higher accuracy and stability than the traditional PNN, SVM, BP, and other methods.
format	article
author	Zhengyu Du Jie Ma Chao Ma Min Huang Weiwei Sun
author_facet	Zhengyu Du Jie Ma Chao Ma Min Huang Weiwei Sun
author_sort	Zhengyu Du
title	Weighted Reconstruction and Improved Eigenclass Combination Method for the Detection of Bearing Faults
title_short	Weighted Reconstruction and Improved Eigenclass Combination Method for the Detection of Bearing Faults
title_full	Weighted Reconstruction and Improved Eigenclass Combination Method for the Detection of Bearing Faults
title_fullStr	Weighted Reconstruction and Improved Eigenclass Combination Method for the Detection of Bearing Faults
title_full_unstemmed	Weighted Reconstruction and Improved Eigenclass Combination Method for the Detection of Bearing Faults
title_sort	weighted reconstruction and improved eigenclass combination method for the detection of bearing faults
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/23fa626fe2d5494581a170587b119874
work_keys_str_mv	AT zhengyudu weightedreconstructionandimprovedeigenclasscombinationmethodforthedetectionofbearingfaults AT jiema weightedreconstructionandimprovedeigenclasscombinationmethodforthedetectionofbearingfaults AT chaoma weightedreconstructionandimprovedeigenclasscombinationmethodforthedetectionofbearingfaults AT minhuang weightedreconstructionandimprovedeigenclasscombinationmethodforthedetectionofbearingfaults AT weiweisun weightedreconstructionandimprovedeigenclasscombinationmethodforthedetectionofbearingfaults
_version_	1718418268012675072

Weighted Reconstruction and Improved Eigenclass Combination Method for the Detection of Bearing Faults

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