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Gearbox Fault Diagnosis Method Based on Multidomain Information Fusion
Traditional methods of gearbox fault diagnosis rely heavily on manual experience. To address this problem, our study proposes a gearbox fault diagnosis method based on multidomain information fusion. An experimental platform consisting of a JZQ250 fixed-axis gearbox was built. An acceleration sensor...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10223782/ https://www.ncbi.nlm.nih.gov/pubmed/37430835 http://dx.doi.org/10.3390/s23104921 |
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author | Xie, Fengyun Wang, Gan Shang, Jiandong Liu, Hui Xiao, Qian Xie, Sanmao |
author_facet | Xie, Fengyun Wang, Gan Shang, Jiandong Liu, Hui Xiao, Qian Xie, Sanmao |
author_sort | Xie, Fengyun |
collection | PubMed |
description | Traditional methods of gearbox fault diagnosis rely heavily on manual experience. To address this problem, our study proposes a gearbox fault diagnosis method based on multidomain information fusion. An experimental platform consisting of a JZQ250 fixed-axis gearbox was built. An acceleration sensor was used to obtain the vibration signal of the gearbox. Singular value decomposition (SVD) was used to preprocess the signal in order to reduce noise, and the processed vibration signal was subjected to short-time Fourier transform to obtain a two-dimensional time–frequency map. A multidomain information fusion convolutional neural network (CNN) model was constructed. Channel 1 was a one-dimensional convolutional neural network (1DCNN) model that input a one-dimensional vibration signal, and channel 2 was a two-dimensional convolutional neural network (2DCNN) model that input short-time Fourier transform (STFT) time–frequency images. The feature vectors extracted using the two channels were then fused into feature vectors for input into the classification model. Finally, support vector machines (SVM) were used to identify and classify the fault types. The model training performance used multiple methods: training set, verification set, loss curve, accuracy curve and t-SNE visualization (t-SNE). Through experimental verification, the method proposed in this paper was compared with FFT-2DCNN, 1DCNN-SVM and 2DCNN-SVM in terms of gearbox fault recognition performance. The model proposed in this paper had the highest fault recognition accuracy (98.08%). |
format | Online Article Text |
id | pubmed-10223782 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102237822023-05-28 Gearbox Fault Diagnosis Method Based on Multidomain Information Fusion Xie, Fengyun Wang, Gan Shang, Jiandong Liu, Hui Xiao, Qian Xie, Sanmao Sensors (Basel) Article Traditional methods of gearbox fault diagnosis rely heavily on manual experience. To address this problem, our study proposes a gearbox fault diagnosis method based on multidomain information fusion. An experimental platform consisting of a JZQ250 fixed-axis gearbox was built. An acceleration sensor was used to obtain the vibration signal of the gearbox. Singular value decomposition (SVD) was used to preprocess the signal in order to reduce noise, and the processed vibration signal was subjected to short-time Fourier transform to obtain a two-dimensional time–frequency map. A multidomain information fusion convolutional neural network (CNN) model was constructed. Channel 1 was a one-dimensional convolutional neural network (1DCNN) model that input a one-dimensional vibration signal, and channel 2 was a two-dimensional convolutional neural network (2DCNN) model that input short-time Fourier transform (STFT) time–frequency images. The feature vectors extracted using the two channels were then fused into feature vectors for input into the classification model. Finally, support vector machines (SVM) were used to identify and classify the fault types. The model training performance used multiple methods: training set, verification set, loss curve, accuracy curve and t-SNE visualization (t-SNE). Through experimental verification, the method proposed in this paper was compared with FFT-2DCNN, 1DCNN-SVM and 2DCNN-SVM in terms of gearbox fault recognition performance. The model proposed in this paper had the highest fault recognition accuracy (98.08%). MDPI 2023-05-19 /pmc/articles/PMC10223782/ /pubmed/37430835 http://dx.doi.org/10.3390/s23104921 Text en © 2023 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 Xie, Fengyun Wang, Gan Shang, Jiandong Liu, Hui Xiao, Qian Xie, Sanmao Gearbox Fault Diagnosis Method Based on Multidomain Information Fusion |
title | Gearbox Fault Diagnosis Method Based on Multidomain Information Fusion |
title_full | Gearbox Fault Diagnosis Method Based on Multidomain Information Fusion |
title_fullStr | Gearbox Fault Diagnosis Method Based on Multidomain Information Fusion |
title_full_unstemmed | Gearbox Fault Diagnosis Method Based on Multidomain Information Fusion |
title_short | Gearbox Fault Diagnosis Method Based on Multidomain Information Fusion |
title_sort | gearbox fault diagnosis method based on multidomain information fusion |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10223782/ https://www.ncbi.nlm.nih.gov/pubmed/37430835 http://dx.doi.org/10.3390/s23104921 |
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