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Fault detection of gearbox by multivariate extended variational mode decomposition-based time–frequency images and incremental RVM algorithm
A novel detection method based on multivariate extended variational mode decomposition-based time–frequency images and incremental RVM algorithm (MEVMDTFI–IRVM) is presented for fault detection of gearbox. The time–frequency images are constructed by multivariate extended variational mode decomposit...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10188441/ https://www.ncbi.nlm.nih.gov/pubmed/37193764 http://dx.doi.org/10.1038/s41598-023-34868-4 |
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author | Nao, Siwei Wang, Yan |
author_facet | Nao, Siwei Wang, Yan |
author_sort | Nao, Siwei |
collection | PubMed |
description | A novel detection method based on multivariate extended variational mode decomposition-based time–frequency images and incremental RVM algorithm (MEVMDTFI–IRVM) is presented for fault detection of gearbox. The time–frequency images are constructed by multivariate extended variational mode decomposition. Compared with single-variable modal decomposition method, multivariate extended variational mode decomposition not only has an accurate mathematical framework, but also has good robustness to non-stationary multi-channel signals with low signal-to-noise ratio. The incremental RVM algorithm is presented for fault detection of gearbox based on the time–frequency images constructed by multivariate extended variational mode decomposition. The testing results demonstrate that the detection results of MEVMDTFI–IRVM for gearbox are stable, in addition, the detection results of MEVMDTFI–IRVM for gearbox are better than those of variational mode decomposition-based time–frequency images and incremental RVM algorithm (VMDTFI–IRVM), variational mode decomposition–RVM algorithm (VMD–RVM), and traditional RVM algorithm. |
format | Online Article Text |
id | pubmed-10188441 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-101884412023-05-18 Fault detection of gearbox by multivariate extended variational mode decomposition-based time–frequency images and incremental RVM algorithm Nao, Siwei Wang, Yan Sci Rep Article A novel detection method based on multivariate extended variational mode decomposition-based time–frequency images and incremental RVM algorithm (MEVMDTFI–IRVM) is presented for fault detection of gearbox. The time–frequency images are constructed by multivariate extended variational mode decomposition. Compared with single-variable modal decomposition method, multivariate extended variational mode decomposition not only has an accurate mathematical framework, but also has good robustness to non-stationary multi-channel signals with low signal-to-noise ratio. The incremental RVM algorithm is presented for fault detection of gearbox based on the time–frequency images constructed by multivariate extended variational mode decomposition. The testing results demonstrate that the detection results of MEVMDTFI–IRVM for gearbox are stable, in addition, the detection results of MEVMDTFI–IRVM for gearbox are better than those of variational mode decomposition-based time–frequency images and incremental RVM algorithm (VMDTFI–IRVM), variational mode decomposition–RVM algorithm (VMD–RVM), and traditional RVM algorithm. Nature Publishing Group UK 2023-05-16 /pmc/articles/PMC10188441/ /pubmed/37193764 http://dx.doi.org/10.1038/s41598-023-34868-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Nao, Siwei Wang, Yan Fault detection of gearbox by multivariate extended variational mode decomposition-based time–frequency images and incremental RVM algorithm |
title | Fault detection of gearbox by multivariate extended variational mode decomposition-based time–frequency images and incremental RVM algorithm |
title_full | Fault detection of gearbox by multivariate extended variational mode decomposition-based time–frequency images and incremental RVM algorithm |
title_fullStr | Fault detection of gearbox by multivariate extended variational mode decomposition-based time–frequency images and incremental RVM algorithm |
title_full_unstemmed | Fault detection of gearbox by multivariate extended variational mode decomposition-based time–frequency images and incremental RVM algorithm |
title_short | Fault detection of gearbox by multivariate extended variational mode decomposition-based time–frequency images and incremental RVM algorithm |
title_sort | fault detection of gearbox by multivariate extended variational mode decomposition-based time–frequency images and incremental rvm algorithm |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10188441/ https://www.ncbi.nlm.nih.gov/pubmed/37193764 http://dx.doi.org/10.1038/s41598-023-34868-4 |
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