An Improved CEEMDAN Time-Domain Energy Entropy Method for the Failure Mode Identification of the Rolling Bearing

As a key component of a mechanical system, the extraction and accurate identification of rolling bearing fault feature information are of great importance to guarantee the normal operation of the mechanical system. Aiming at that the extraction of rolling bearing fault features and traditional suppo...

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Autores principales:	Fengfeng Bie, Yi Miao, Fengxia Lyu, Jian Peng, Yue Guo
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Physics QC1-999
Acceso en línea:	https://doaj.org/article/999dda545d9b44e984ecfbb7a4d37c76
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spelling	oai:doaj.org-article:999dda545d9b44e984ecfbb7a4d37c762021-11-22T01:09:55ZAn Improved CEEMDAN Time-Domain Energy Entropy Method for the Failure Mode Identification of the Rolling Bearing1875-920310.1155/2021/7461402https://doaj.org/article/999dda545d9b44e984ecfbb7a4d37c762021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/7461402https://doaj.org/toc/1875-9203As a key component of a mechanical system, the extraction and accurate identification of rolling bearing fault feature information are of great importance to guarantee the normal operation of the mechanical system. Aiming at that the extraction of rolling bearing fault features and traditional support vector machine parameters affects the overall accuracy of pattern classification, the improved CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise) time-domain energy entropy-based model for fault pattern recognition is proposed. The ICEEMDAN method is developed to decompose the signal to obtain the IMF component series. Then, the particular IMF components are selected according to the trend of correlation coefficient and variance contribution rate; meanwhile, the information entropy (power spectral entropy, singular spectral entropy, and time-domain energy entropy) of the screened IMF components is calculated to construct the feature vector sets, respectively. Finally, the feature vector sets are input into the PSO-SVM (particle swarm optimization-support vector machine) based model for training and pattern recognition. The effectiveness of the proposed method of the improved CEEMDAN time-domain energy entropy and PSO-SVM model is testified through numerical simulation and experiments on rolling bearing datasets. The comparison proceeded with the other mainstream intelligent recognition techniques indicates the superiority of the method with the diagnostic accuracy of 100% as for the final validation.Fengfeng BieYi MiaoFengxia LyuJian PengYue GuoHindawi LimitedarticlePhysicsQC1-999ENShock and Vibration, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Physics QC1-999
spellingShingle	Physics QC1-999 Fengfeng Bie Yi Miao Fengxia Lyu Jian Peng Yue Guo An Improved CEEMDAN Time-Domain Energy Entropy Method for the Failure Mode Identification of the Rolling Bearing
description	As a key component of a mechanical system, the extraction and accurate identification of rolling bearing fault feature information are of great importance to guarantee the normal operation of the mechanical system. Aiming at that the extraction of rolling bearing fault features and traditional support vector machine parameters affects the overall accuracy of pattern classification, the improved CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise) time-domain energy entropy-based model for fault pattern recognition is proposed. The ICEEMDAN method is developed to decompose the signal to obtain the IMF component series. Then, the particular IMF components are selected according to the trend of correlation coefficient and variance contribution rate; meanwhile, the information entropy (power spectral entropy, singular spectral entropy, and time-domain energy entropy) of the screened IMF components is calculated to construct the feature vector sets, respectively. Finally, the feature vector sets are input into the PSO-SVM (particle swarm optimization-support vector machine) based model for training and pattern recognition. The effectiveness of the proposed method of the improved CEEMDAN time-domain energy entropy and PSO-SVM model is testified through numerical simulation and experiments on rolling bearing datasets. The comparison proceeded with the other mainstream intelligent recognition techniques indicates the superiority of the method with the diagnostic accuracy of 100% as for the final validation.
format	article
author	Fengfeng Bie Yi Miao Fengxia Lyu Jian Peng Yue Guo
author_facet	Fengfeng Bie Yi Miao Fengxia Lyu Jian Peng Yue Guo
author_sort	Fengfeng Bie
title	An Improved CEEMDAN Time-Domain Energy Entropy Method for the Failure Mode Identification of the Rolling Bearing
title_short	An Improved CEEMDAN Time-Domain Energy Entropy Method for the Failure Mode Identification of the Rolling Bearing
title_full	An Improved CEEMDAN Time-Domain Energy Entropy Method for the Failure Mode Identification of the Rolling Bearing
title_fullStr	An Improved CEEMDAN Time-Domain Energy Entropy Method for the Failure Mode Identification of the Rolling Bearing
title_full_unstemmed	An Improved CEEMDAN Time-Domain Energy Entropy Method for the Failure Mode Identification of the Rolling Bearing
title_sort	improved ceemdan time-domain energy entropy method for the failure mode identification of the rolling bearing
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/999dda545d9b44e984ecfbb7a4d37c76
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An Improved CEEMDAN Time-Domain Energy Entropy Method for the Failure Mode Identification of the Rolling Bearing

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