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A New Monitoring Technology for Bearing Fault Detection in High-Speed Trains
In this work, a new monitoring system is developed for bearing fault detection in high-speed trains. Firstly, a data acquisition system is developed to collect vibration and other related signals wirelessly. Secondly, a new multiple correlation analysis (MCA) technique is proposed for bearing fault...
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/PMC10385460/ https://www.ncbi.nlm.nih.gov/pubmed/37514687 http://dx.doi.org/10.3390/s23146392 |
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author | Sun, Sitong Zhang, Sheng Wang, Wilson |
author_facet | Sun, Sitong Zhang, Sheng Wang, Wilson |
author_sort | Sun, Sitong |
collection | PubMed |
description | In this work, a new monitoring system is developed for bearing fault detection in high-speed trains. Firstly, a data acquisition system is developed to collect vibration and other related signals wirelessly. Secondly, a new multiple correlation analysis (MCA) technique is proposed for bearing fault detection. The MCA technique consists of the three processing steps: (1) the collected vibration signal is decomposed by variational modal decomposition (VMD) to formulate the representative intrinsic mode functions (IMFs); (2) the MCA is used to process and identify the characteristic features for signal analysis; (3) bearing fault is diagnosed by examining bearing characteristic frequency information on the envelope power spectrum. The effectiveness of the proposed MCA fault detection technique is verified by experimental tests corresponding to different bearing conditions. |
format | Online Article Text |
id | pubmed-10385460 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103854602023-07-30 A New Monitoring Technology for Bearing Fault Detection in High-Speed Trains Sun, Sitong Zhang, Sheng Wang, Wilson Sensors (Basel) Article In this work, a new monitoring system is developed for bearing fault detection in high-speed trains. Firstly, a data acquisition system is developed to collect vibration and other related signals wirelessly. Secondly, a new multiple correlation analysis (MCA) technique is proposed for bearing fault detection. The MCA technique consists of the three processing steps: (1) the collected vibration signal is decomposed by variational modal decomposition (VMD) to formulate the representative intrinsic mode functions (IMFs); (2) the MCA is used to process and identify the characteristic features for signal analysis; (3) bearing fault is diagnosed by examining bearing characteristic frequency information on the envelope power spectrum. The effectiveness of the proposed MCA fault detection technique is verified by experimental tests corresponding to different bearing conditions. MDPI 2023-07-14 /pmc/articles/PMC10385460/ /pubmed/37514687 http://dx.doi.org/10.3390/s23146392 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 Sun, Sitong Zhang, Sheng Wang, Wilson A New Monitoring Technology for Bearing Fault Detection in High-Speed Trains |
title | A New Monitoring Technology for Bearing Fault Detection in High-Speed Trains |
title_full | A New Monitoring Technology for Bearing Fault Detection in High-Speed Trains |
title_fullStr | A New Monitoring Technology for Bearing Fault Detection in High-Speed Trains |
title_full_unstemmed | A New Monitoring Technology for Bearing Fault Detection in High-Speed Trains |
title_short | A New Monitoring Technology for Bearing Fault Detection in High-Speed Trains |
title_sort | new monitoring technology for bearing fault detection in high-speed trains |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385460/ https://www.ncbi.nlm.nih.gov/pubmed/37514687 http://dx.doi.org/10.3390/s23146392 |
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