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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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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. |
format | Online Article Text |
id | pubmed-7828531 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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