A Bearing Fault Diagnosis Method Based on PAVME and MEDE

When rolling bearings have a local fault, the real bearing vibration signal related to the local fault is characterized by the properties of nonlinear and nonstationary. To extract the useful fault features from the collected nonlinear and nonstationary bearing vibration signals and improve diagnost...

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Autores principales:	Xiaoan Yan, Yadong Xu, Daoming She, Wan Zhang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	variational mode extraction multiscale envelope dispersion entropy rolling bearing fault diagnosis Science Q Astrophysics QB460-466 Physics QC1-999
Acceso en línea:	https://doaj.org/article/42d13214d87f4c8c995fe2e43cc4387d
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spelling	oai:doaj.org-article:42d13214d87f4c8c995fe2e43cc4387d2021-11-25T17:29:21ZA Bearing Fault Diagnosis Method Based on PAVME and MEDE10.3390/e231114021099-4300https://doaj.org/article/42d13214d87f4c8c995fe2e43cc4387d2021-10-01T00:00:00Zhttps://www.mdpi.com/1099-4300/23/11/1402https://doaj.org/toc/1099-4300When rolling bearings have a local fault, the real bearing vibration signal related to the local fault is characterized by the properties of nonlinear and nonstationary. To extract the useful fault features from the collected nonlinear and nonstationary bearing vibration signals and improve diagnostic accuracy, this paper proposes a new bearing fault diagnosis method based on parameter adaptive variational mode extraction (PAVME) and multiscale envelope dispersion entropy (MEDE). Firstly, a new method hailed as parameter adaptive variational mode extraction (PAVME) is presented to process the collected original bearing vibration signal and obtain the frequency components related to bearing faults, where its two important parameters (i.e., the penalty factor and mode center-frequency) are automatically determined by whale optimization algorithm. Subsequently, based on the processed bearing vibration signal, an effective complexity evaluation approach named multiscale envelope dispersion entropy (MEDE) is calculated for conducting bearing fault feature extraction. Finally, the extracted fault features are fed into the k-nearest neighbor (KNN) to automatically identify different health conditions of rolling bearing. Case studies and contrastive analysis are performed to validate the effectiveness and superiority of the proposed method. Experimental results show that the proposed method can not only effectively extract bearing fault features, but also obtain a high identification accuracy for bearing fault patterns under single or variable speed.Xiaoan YanYadong XuDaoming SheWan ZhangMDPI AGarticlevariational mode extractionmultiscale envelope dispersion entropyrolling bearingfault diagnosisScienceQAstrophysicsQB460-466PhysicsQC1-999ENEntropy, Vol 23, Iss 1402, p 1402 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	variational mode extraction multiscale envelope dispersion entropy rolling bearing fault diagnosis Science Q Astrophysics QB460-466 Physics QC1-999
spellingShingle	variational mode extraction multiscale envelope dispersion entropy rolling bearing fault diagnosis Science Q Astrophysics QB460-466 Physics QC1-999 Xiaoan Yan Yadong Xu Daoming She Wan Zhang A Bearing Fault Diagnosis Method Based on PAVME and MEDE
description	When rolling bearings have a local fault, the real bearing vibration signal related to the local fault is characterized by the properties of nonlinear and nonstationary. To extract the useful fault features from the collected nonlinear and nonstationary bearing vibration signals and improve diagnostic accuracy, this paper proposes a new bearing fault diagnosis method based on parameter adaptive variational mode extraction (PAVME) and multiscale envelope dispersion entropy (MEDE). Firstly, a new method hailed as parameter adaptive variational mode extraction (PAVME) is presented to process the collected original bearing vibration signal and obtain the frequency components related to bearing faults, where its two important parameters (i.e., the penalty factor and mode center-frequency) are automatically determined by whale optimization algorithm. Subsequently, based on the processed bearing vibration signal, an effective complexity evaluation approach named multiscale envelope dispersion entropy (MEDE) is calculated for conducting bearing fault feature extraction. Finally, the extracted fault features are fed into the k-nearest neighbor (KNN) to automatically identify different health conditions of rolling bearing. Case studies and contrastive analysis are performed to validate the effectiveness and superiority of the proposed method. Experimental results show that the proposed method can not only effectively extract bearing fault features, but also obtain a high identification accuracy for bearing fault patterns under single or variable speed.
format	article
author	Xiaoan Yan Yadong Xu Daoming She Wan Zhang
author_facet	Xiaoan Yan Yadong Xu Daoming She Wan Zhang
author_sort	Xiaoan Yan
title	A Bearing Fault Diagnosis Method Based on PAVME and MEDE
title_short	A Bearing Fault Diagnosis Method Based on PAVME and MEDE
title_full	A Bearing Fault Diagnosis Method Based on PAVME and MEDE
title_fullStr	A Bearing Fault Diagnosis Method Based on PAVME and MEDE
title_full_unstemmed	A Bearing Fault Diagnosis Method Based on PAVME and MEDE
title_sort	bearing fault diagnosis method based on pavme and mede
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/42d13214d87f4c8c995fe2e43cc4387d
work_keys_str_mv	AT xiaoanyan abearingfaultdiagnosismethodbasedonpavmeandmede AT yadongxu abearingfaultdiagnosismethodbasedonpavmeandmede AT daomingshe abearingfaultdiagnosismethodbasedonpavmeandmede AT wanzhang abearingfaultdiagnosismethodbasedonpavmeandmede AT xiaoanyan bearingfaultdiagnosismethodbasedonpavmeandmede AT yadongxu bearingfaultdiagnosismethodbasedonpavmeandmede AT daomingshe bearingfaultdiagnosismethodbasedonpavmeandmede AT wanzhang bearingfaultdiagnosismethodbasedonpavmeandmede
_version_	1718412280599674880

A Bearing Fault Diagnosis Method Based on PAVME and MEDE

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