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Application of CNN-Based Machine Learning in the Study of Motor Fault Diagnosis
With the development of science and technology, the rapid development of social economy, the motor as a new type of transmission equipment, in the production and life of people occupies a pivotal position. Under the rapid development of computer and electronic technology, manufacturing equipment is...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9467767/ https://www.ncbi.nlm.nih.gov/pubmed/36105635 http://dx.doi.org/10.1155/2022/9635251 |
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author | Peng, Xiuyan Wei, Lunpan Gao, Wei |
author_facet | Peng, Xiuyan Wei, Lunpan Gao, Wei |
author_sort | Peng, Xiuyan |
collection | PubMed |
description | With the development of science and technology, the rapid development of social economy, the motor as a new type of transmission equipment, in the production and life of people occupies a pivotal position. Under the rapid development of computer and electronic technology, manufacturing equipment is becoming larger, faster, more continuous, and more automated. This has resulted in complex, expensive, accident-damaging, and high-impact equipment for electric motors; even routine maintenance requires significant equipment maintenance and maintenance costs. If a fault occurs, it will cause serious damage to the entire equipment and can even have a major impact on the entire production process, leading to a serious economic and social life. In this paper, a CNN-based machine learning fault diagnosis method is proposed to address the problem of high incidence of motor faults and difficulty in identifying fault types. A fault reproduction test is constructed by machine learning techniques to extract vibration time domain data for normal operating conditions, rotor eccentricity, stator short circuit, and bearing inner ring fault; divide the data segment into 15 speed segments, extract 13 typical time domain features for each speed segment; and perform mathematical statistics for fault diagnosis. Compared with the traditional algorithm, the method has more comprehensive feature information extraction, higher diagnostic accuracy, and faster diagnostic speed, with a fault diagnosis accuracy of 98.7%. |
format | Online Article Text |
id | pubmed-9467767 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-94677672022-09-13 Application of CNN-Based Machine Learning in the Study of Motor Fault Diagnosis Peng, Xiuyan Wei, Lunpan Gao, Wei Comput Intell Neurosci Research Article With the development of science and technology, the rapid development of social economy, the motor as a new type of transmission equipment, in the production and life of people occupies a pivotal position. Under the rapid development of computer and electronic technology, manufacturing equipment is becoming larger, faster, more continuous, and more automated. This has resulted in complex, expensive, accident-damaging, and high-impact equipment for electric motors; even routine maintenance requires significant equipment maintenance and maintenance costs. If a fault occurs, it will cause serious damage to the entire equipment and can even have a major impact on the entire production process, leading to a serious economic and social life. In this paper, a CNN-based machine learning fault diagnosis method is proposed to address the problem of high incidence of motor faults and difficulty in identifying fault types. A fault reproduction test is constructed by machine learning techniques to extract vibration time domain data for normal operating conditions, rotor eccentricity, stator short circuit, and bearing inner ring fault; divide the data segment into 15 speed segments, extract 13 typical time domain features for each speed segment; and perform mathematical statistics for fault diagnosis. Compared with the traditional algorithm, the method has more comprehensive feature information extraction, higher diagnostic accuracy, and faster diagnostic speed, with a fault diagnosis accuracy of 98.7%. Hindawi 2022-09-05 /pmc/articles/PMC9467767/ /pubmed/36105635 http://dx.doi.org/10.1155/2022/9635251 Text en Copyright © 2022 Xiuyan Peng 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 Peng, Xiuyan Wei, Lunpan Gao, Wei Application of CNN-Based Machine Learning in the Study of Motor Fault Diagnosis |
title | Application of CNN-Based Machine Learning in the Study of Motor Fault Diagnosis |
title_full | Application of CNN-Based Machine Learning in the Study of Motor Fault Diagnosis |
title_fullStr | Application of CNN-Based Machine Learning in the Study of Motor Fault Diagnosis |
title_full_unstemmed | Application of CNN-Based Machine Learning in the Study of Motor Fault Diagnosis |
title_short | Application of CNN-Based Machine Learning in the Study of Motor Fault Diagnosis |
title_sort | application of cnn-based machine learning in the study of motor fault diagnosis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9467767/ https://www.ncbi.nlm.nih.gov/pubmed/36105635 http://dx.doi.org/10.1155/2022/9635251 |
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