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Intelligent Ball Bearing Fault Diagnosis Using Fractional Lorenz Chaos Extension Detection
In this study we used a non-autonomous Chua’s circuit, and the fractional Lorenz chaos system. This was combined with the Extension theory detection method to analyze the voltage signals. The bearing vibration signals, measured using an acceleration sensor, were introduced into the master and slave...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6164396/ https://www.ncbi.nlm.nih.gov/pubmed/30213131 http://dx.doi.org/10.3390/s18093069 |
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author | Tian, An-Hong Fu, Cheng-Biao Li, Yu-Chung Yau, Her-Terng |
author_facet | Tian, An-Hong Fu, Cheng-Biao Li, Yu-Chung Yau, Her-Terng |
author_sort | Tian, An-Hong |
collection | PubMed |
description | In this study we used a non-autonomous Chua’s circuit, and the fractional Lorenz chaos system. This was combined with the Extension theory detection method to analyze the voltage signals. The bearing vibration signals, measured using an acceleration sensor, were introduced into the master and slave systems through a Chua’s circuit. In a chaotic system, minor differences can cause significant changes that generate dynamic errors. The matter-element model extension can be used to determine the bearing condition. Extension theory can be used to establish classical and sectional domains using the dynamic errors of the fault conditions. The results obtained were compared with those from discrete Fourier transform analysis, wavelet analysis and an integer order chaos system. The diagnostic rate of the fractional-order master and slave chaotic system could reach 100% if the fractional-order parameter adjustment was used. This study presents a very efficient and inexpensive method for monitoring the state of ball bearings. |
format | Online Article Text |
id | pubmed-6164396 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-61643962018-10-10 Intelligent Ball Bearing Fault Diagnosis Using Fractional Lorenz Chaos Extension Detection Tian, An-Hong Fu, Cheng-Biao Li, Yu-Chung Yau, Her-Terng Sensors (Basel) Article In this study we used a non-autonomous Chua’s circuit, and the fractional Lorenz chaos system. This was combined with the Extension theory detection method to analyze the voltage signals. The bearing vibration signals, measured using an acceleration sensor, were introduced into the master and slave systems through a Chua’s circuit. In a chaotic system, minor differences can cause significant changes that generate dynamic errors. The matter-element model extension can be used to determine the bearing condition. Extension theory can be used to establish classical and sectional domains using the dynamic errors of the fault conditions. The results obtained were compared with those from discrete Fourier transform analysis, wavelet analysis and an integer order chaos system. The diagnostic rate of the fractional-order master and slave chaotic system could reach 100% if the fractional-order parameter adjustment was used. This study presents a very efficient and inexpensive method for monitoring the state of ball bearings. MDPI 2018-09-12 /pmc/articles/PMC6164396/ /pubmed/30213131 http://dx.doi.org/10.3390/s18093069 Text en © 2018 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Tian, An-Hong Fu, Cheng-Biao Li, Yu-Chung Yau, Her-Terng Intelligent Ball Bearing Fault Diagnosis Using Fractional Lorenz Chaos Extension Detection |
title | Intelligent Ball Bearing Fault Diagnosis Using Fractional Lorenz Chaos Extension Detection |
title_full | Intelligent Ball Bearing Fault Diagnosis Using Fractional Lorenz Chaos Extension Detection |
title_fullStr | Intelligent Ball Bearing Fault Diagnosis Using Fractional Lorenz Chaos Extension Detection |
title_full_unstemmed | Intelligent Ball Bearing Fault Diagnosis Using Fractional Lorenz Chaos Extension Detection |
title_short | Intelligent Ball Bearing Fault Diagnosis Using Fractional Lorenz Chaos Extension Detection |
title_sort | intelligent ball bearing fault diagnosis using fractional lorenz chaos extension detection |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6164396/ https://www.ncbi.nlm.nih.gov/pubmed/30213131 http://dx.doi.org/10.3390/s18093069 |
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