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Improved Variational Mode Decomposition and CNN for Intelligent Rotating Machinery Fault Diagnosis
This paper proposes an intelligent diagnosis method for rotating machinery faults based on improved variational mode decomposition (IVMD) and CNN to process the rotating machinery non-stationary signal. Firstly, to solve the problem of time-domain feature extraction for fault diagnosis, this paper p...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9317035/ https://www.ncbi.nlm.nih.gov/pubmed/35885131 http://dx.doi.org/10.3390/e24070908 |
_version_ | 1784754959584067584 |
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author | Xiao, Qiyang Li, Sen Zhou, Lin Shi, Wentao |
author_facet | Xiao, Qiyang Li, Sen Zhou, Lin Shi, Wentao |
author_sort | Xiao, Qiyang |
collection | PubMed |
description | This paper proposes an intelligent diagnosis method for rotating machinery faults based on improved variational mode decomposition (IVMD) and CNN to process the rotating machinery non-stationary signal. Firstly, to solve the problem of time-domain feature extraction for fault diagnosis, this paper proposes an improved variational mode decomposition method with automatic optimization of the number of modes. This method overcomes the problems of the traditional VMD method, in that each parameter is set by experience and is greatly influenced by subjective experience. Secondly, the decomposed signal components are analyzed by correlation, and then high correlated components with the original signal are selected to reconstruct the original signal. The continuous wavelet transform (CWT) is employed to extract the two-dimensional time–frequency domain feature map of the fault signal. Finally, the deep learning method is used to construct a convolutional neural network. After feature extraction, the two-dimensional time-frequency image is applied to the neural network to identify fault features. Experiments verify that the proposed method can adapt to rotating machinery faults in complex environments and has a high recognition rate. |
format | Online Article Text |
id | pubmed-9317035 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93170352022-07-27 Improved Variational Mode Decomposition and CNN for Intelligent Rotating Machinery Fault Diagnosis Xiao, Qiyang Li, Sen Zhou, Lin Shi, Wentao Entropy (Basel) Article This paper proposes an intelligent diagnosis method for rotating machinery faults based on improved variational mode decomposition (IVMD) and CNN to process the rotating machinery non-stationary signal. Firstly, to solve the problem of time-domain feature extraction for fault diagnosis, this paper proposes an improved variational mode decomposition method with automatic optimization of the number of modes. This method overcomes the problems of the traditional VMD method, in that each parameter is set by experience and is greatly influenced by subjective experience. Secondly, the decomposed signal components are analyzed by correlation, and then high correlated components with the original signal are selected to reconstruct the original signal. The continuous wavelet transform (CWT) is employed to extract the two-dimensional time–frequency domain feature map of the fault signal. Finally, the deep learning method is used to construct a convolutional neural network. After feature extraction, the two-dimensional time-frequency image is applied to the neural network to identify fault features. Experiments verify that the proposed method can adapt to rotating machinery faults in complex environments and has a high recognition rate. MDPI 2022-06-30 /pmc/articles/PMC9317035/ /pubmed/35885131 http://dx.doi.org/10.3390/e24070908 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Xiao, Qiyang Li, Sen Zhou, Lin Shi, Wentao Improved Variational Mode Decomposition and CNN for Intelligent Rotating Machinery Fault Diagnosis |
title | Improved Variational Mode Decomposition and CNN for Intelligent Rotating Machinery Fault Diagnosis |
title_full | Improved Variational Mode Decomposition and CNN for Intelligent Rotating Machinery Fault Diagnosis |
title_fullStr | Improved Variational Mode Decomposition and CNN for Intelligent Rotating Machinery Fault Diagnosis |
title_full_unstemmed | Improved Variational Mode Decomposition and CNN for Intelligent Rotating Machinery Fault Diagnosis |
title_short | Improved Variational Mode Decomposition and CNN for Intelligent Rotating Machinery Fault Diagnosis |
title_sort | improved variational mode decomposition and cnn for intelligent rotating machinery fault diagnosis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9317035/ https://www.ncbi.nlm.nih.gov/pubmed/35885131 http://dx.doi.org/10.3390/e24070908 |
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