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An Optimal Parameter Selection Method for MOMEDA Based on EHNR and Its Spectral Entropy

As a vital component widely used in the industrial production field, rolling bearings work under complicated working conditions and are prone to failure, which will affect the normal operation of the whole mechanical system. Therefore, it is essential to conduct a health assessment of the rolling be...

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Detalles Bibliográficos
Autores principales: Li, Zhuorui, Ma, Jun, Wang, Xiaodong, Li, Xiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7828531/
https://www.ncbi.nlm.nih.gov/pubmed/33451014
http://dx.doi.org/10.3390/s21020533
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author Li, Zhuorui
Ma, Jun
Wang, Xiaodong
Li, Xiang
author_facet Li, Zhuorui
Ma, Jun
Wang, Xiaodong
Li, Xiang
author_sort Li, Zhuorui
collection PubMed
description As a vital component widely used in the industrial production field, rolling bearings work under complicated working conditions and are prone to failure, which will affect the normal operation of the whole mechanical system. Therefore, it is essential to conduct a health assessment of the rolling bearing. In recent years, Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) is applied to the fault feature extraction for rolling bearings. However, the algorithm still has the following problems: (1) The selection of fault period T depends on prior knowledge. (2) The accuracy of signal denoising is affected by filter length L. To solve the limitations, an improved MOMEDA (IMOMEDA) method is proposed in this paper. Firstly, the envelope harmonic-to-noise ratio (EHNR) spectrum is adopted to estimate the fault period of MOMEDA. Then, the improved grid search method with EHNR spectral entropy as the objective function is constructed to calculate the optimal filter length used in the MOMEDA. Finally, a feature extraction method based on the improved MOMEDA (IMOMEDA) and Teager-Kaiser energy operator (TKEO) is applied in the field of rolling bearing fault diagnosis. The effectiveness and generalization performance of the proposed method is verified through comparison experiment with three data sets.
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spelling pubmed-78285312021-01-25 An Optimal Parameter Selection Method for MOMEDA Based on EHNR and Its Spectral Entropy Li, Zhuorui Ma, Jun Wang, Xiaodong Li, Xiang Sensors (Basel) Article As a vital component widely used in the industrial production field, rolling bearings work under complicated working conditions and are prone to failure, which will affect the normal operation of the whole mechanical system. Therefore, it is essential to conduct a health assessment of the rolling bearing. In recent years, Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) is applied to the fault feature extraction for rolling bearings. However, the algorithm still has the following problems: (1) The selection of fault period T depends on prior knowledge. (2) The accuracy of signal denoising is affected by filter length L. To solve the limitations, an improved MOMEDA (IMOMEDA) method is proposed in this paper. Firstly, the envelope harmonic-to-noise ratio (EHNR) spectrum is adopted to estimate the fault period of MOMEDA. Then, the improved grid search method with EHNR spectral entropy as the objective function is constructed to calculate the optimal filter length used in the MOMEDA. Finally, a feature extraction method based on the improved MOMEDA (IMOMEDA) and Teager-Kaiser energy operator (TKEO) is applied in the field of rolling bearing fault diagnosis. The effectiveness and generalization performance of the proposed method is verified through comparison experiment with three data sets. MDPI 2021-01-13 /pmc/articles/PMC7828531/ /pubmed/33451014 http://dx.doi.org/10.3390/s21020533 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Li, Zhuorui
Ma, Jun
Wang, Xiaodong
Li, Xiang
An Optimal Parameter Selection Method for MOMEDA Based on EHNR and Its Spectral Entropy
title An Optimal Parameter Selection Method for MOMEDA Based on EHNR and Its Spectral Entropy
title_full An Optimal Parameter Selection Method for MOMEDA Based on EHNR and Its Spectral Entropy
title_fullStr An Optimal Parameter Selection Method for MOMEDA Based on EHNR and Its Spectral Entropy
title_full_unstemmed An Optimal Parameter Selection Method for MOMEDA Based on EHNR and Its Spectral Entropy
title_short An Optimal Parameter Selection Method for MOMEDA Based on EHNR and Its Spectral Entropy
title_sort optimal parameter selection method for momeda based on ehnr and its spectral entropy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7828531/
https://www.ncbi.nlm.nih.gov/pubmed/33451014
http://dx.doi.org/10.3390/s21020533
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