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A Novel Fault Diagnosis Method of Rolling Bearing Based on Integrated Vision Transformer Model
In order to improve the diagnosis accuracy and generalization of bearing faults, an integrated vision transformer (ViT) model based on wavelet transform and the soft voting method is proposed in this paper. Firstly, the discrete wavelet transform (DWT) was utilized to decompose the vibration signal...
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/PMC9144877/ https://www.ncbi.nlm.nih.gov/pubmed/35632289 http://dx.doi.org/10.3390/s22103878 |
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author | Tang, Xinyu Xu, Zengbing Wang, Zhigang |
author_facet | Tang, Xinyu Xu, Zengbing Wang, Zhigang |
author_sort | Tang, Xinyu |
collection | PubMed |
description | In order to improve the diagnosis accuracy and generalization of bearing faults, an integrated vision transformer (ViT) model based on wavelet transform and the soft voting method is proposed in this paper. Firstly, the discrete wavelet transform (DWT) was utilized to decompose the vibration signal into the subsignals in the different frequency bands, and then these different subsignals were transformed into a time–frequency representation (TFR) map by the continuous wavelet transform (CWT) method. Secondly, the TFR maps were input with respective to the multiple individual ViT models for preliminary diagnosis analysis. Finally, the final diagnosis decision was obtained by using the soft voting method to fuse all the preliminary diagnosis results. Through multifaceted diagnosis tests of rolling bearings on different datasets, the diagnosis results demonstrate that the proposed integrated ViT model based on the soft voting method can diagnose the different fault categories and fault severities of bearings accurately, and has a higher diagnostic accuracy and generalization ability by comparison analysis with integrated CNN and individual ViT. |
format | Online Article Text |
id | pubmed-9144877 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91448772022-05-29 A Novel Fault Diagnosis Method of Rolling Bearing Based on Integrated Vision Transformer Model Tang, Xinyu Xu, Zengbing Wang, Zhigang Sensors (Basel) Article In order to improve the diagnosis accuracy and generalization of bearing faults, an integrated vision transformer (ViT) model based on wavelet transform and the soft voting method is proposed in this paper. Firstly, the discrete wavelet transform (DWT) was utilized to decompose the vibration signal into the subsignals in the different frequency bands, and then these different subsignals were transformed into a time–frequency representation (TFR) map by the continuous wavelet transform (CWT) method. Secondly, the TFR maps were input with respective to the multiple individual ViT models for preliminary diagnosis analysis. Finally, the final diagnosis decision was obtained by using the soft voting method to fuse all the preliminary diagnosis results. Through multifaceted diagnosis tests of rolling bearings on different datasets, the diagnosis results demonstrate that the proposed integrated ViT model based on the soft voting method can diagnose the different fault categories and fault severities of bearings accurately, and has a higher diagnostic accuracy and generalization ability by comparison analysis with integrated CNN and individual ViT. MDPI 2022-05-20 /pmc/articles/PMC9144877/ /pubmed/35632289 http://dx.doi.org/10.3390/s22103878 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 Tang, Xinyu Xu, Zengbing Wang, Zhigang A Novel Fault Diagnosis Method of Rolling Bearing Based on Integrated Vision Transformer Model |
title | A Novel Fault Diagnosis Method of Rolling Bearing Based on Integrated Vision Transformer Model |
title_full | A Novel Fault Diagnosis Method of Rolling Bearing Based on Integrated Vision Transformer Model |
title_fullStr | A Novel Fault Diagnosis Method of Rolling Bearing Based on Integrated Vision Transformer Model |
title_full_unstemmed | A Novel Fault Diagnosis Method of Rolling Bearing Based on Integrated Vision Transformer Model |
title_short | A Novel Fault Diagnosis Method of Rolling Bearing Based on Integrated Vision Transformer Model |
title_sort | novel fault diagnosis method of rolling bearing based on integrated vision transformer model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9144877/ https://www.ncbi.nlm.nih.gov/pubmed/35632289 http://dx.doi.org/10.3390/s22103878 |
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