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The Feature Recognition of Motor Noise Based on the Improved EEMD Model

The noise generated by the machine is closely related to the running state of the machine, so the product can be effectively detected by analyzing the noise signal. The noise identification and control methods based on the EEMD model are widely used in motor noise control. However, the EEMD only con...

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Detalles Bibliográficos
Autores principales: Liao, Qing, Li, Long, Luo, Jiaqi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9410936/
https://www.ncbi.nlm.nih.gov/pubmed/36035816
http://dx.doi.org/10.1155/2022/9172719
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author Liao, Qing
Li, Long
Luo, Jiaqi
author_facet Liao, Qing
Li, Long
Luo, Jiaqi
author_sort Liao, Qing
collection PubMed
description The noise generated by the machine is closely related to the running state of the machine, so the product can be effectively detected by analyzing the noise signal. The noise identification and control methods based on the EEMD model are widely used in motor noise control. However, the EEMD only considers the influence of noise amplitude on the decomposition results, and the added white noise cannot be completely neutralized. In this paper, an improved EEMD method is proposed by analyzing the influence of the maximum frequency on the decomposition results, in which the noise with different maximum frequency and amplitude is added to decompose the signal, and the decomposition effect is judged by the orthogonality coefficient of the decomposition result. Finally, the simulation signal and the measured signal are compared and analyzed, and the results show that the improved EEMD method has some advantages over the original method in suppressing mode confusion and fault diagnosis.
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spelling pubmed-94109362022-08-26 The Feature Recognition of Motor Noise Based on the Improved EEMD Model Liao, Qing Li, Long Luo, Jiaqi Comput Intell Neurosci Research Article The noise generated by the machine is closely related to the running state of the machine, so the product can be effectively detected by analyzing the noise signal. The noise identification and control methods based on the EEMD model are widely used in motor noise control. However, the EEMD only considers the influence of noise amplitude on the decomposition results, and the added white noise cannot be completely neutralized. In this paper, an improved EEMD method is proposed by analyzing the influence of the maximum frequency on the decomposition results, in which the noise with different maximum frequency and amplitude is added to decompose the signal, and the decomposition effect is judged by the orthogonality coefficient of the decomposition result. Finally, the simulation signal and the measured signal are compared and analyzed, and the results show that the improved EEMD method has some advantages over the original method in suppressing mode confusion and fault diagnosis. Hindawi 2022-08-18 /pmc/articles/PMC9410936/ /pubmed/36035816 http://dx.doi.org/10.1155/2022/9172719 Text en Copyright © 2022 Qing Liao et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Liao, Qing
Li, Long
Luo, Jiaqi
The Feature Recognition of Motor Noise Based on the Improved EEMD Model
title The Feature Recognition of Motor Noise Based on the Improved EEMD Model
title_full The Feature Recognition of Motor Noise Based on the Improved EEMD Model
title_fullStr The Feature Recognition of Motor Noise Based on the Improved EEMD Model
title_full_unstemmed The Feature Recognition of Motor Noise Based on the Improved EEMD Model
title_short The Feature Recognition of Motor Noise Based on the Improved EEMD Model
title_sort feature recognition of motor noise based on the improved eemd model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9410936/
https://www.ncbi.nlm.nih.gov/pubmed/36035816
http://dx.doi.org/10.1155/2022/9172719
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