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Intelligent Gearbox Diagnosis Methods Based on SVM, Wavelet Lifting and RBR
Given the problems in intelligent gearbox diagnosis methods, it is difficult to obtain the desired information and a large enough sample size to study; therefore, we propose the application of various methods for gearbox fault diagnosis, including wavelet lifting, a support vector machine (SVM) and...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Molecular Diversity Preservation International (MDPI)
2010
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3292134/ https://www.ncbi.nlm.nih.gov/pubmed/22399894 http://dx.doi.org/10.3390/s100504602 |
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author | Gao, Lixin Ren, Zhiqiang Tang, Wenliang Wang, Huaqing Chen, Peng |
author_facet | Gao, Lixin Ren, Zhiqiang Tang, Wenliang Wang, Huaqing Chen, Peng |
author_sort | Gao, Lixin |
collection | PubMed |
description | Given the problems in intelligent gearbox diagnosis methods, it is difficult to obtain the desired information and a large enough sample size to study; therefore, we propose the application of various methods for gearbox fault diagnosis, including wavelet lifting, a support vector machine (SVM) and rule-based reasoning (RBR). In a complex field environment, it is less likely for machines to have the same fault; moreover, the fault features can also vary. Therefore, a SVM could be used for the initial diagnosis. First, gearbox vibration signals were processed with wavelet packet decomposition, and the signal energy coefficients of each frequency band were extracted and used as input feature vectors in SVM for normal and faulty pattern recognition. Second, precision analysis using wavelet lifting could successfully filter out the noisy signals while maintaining the impulse characteristics of the fault; thus effectively extracting the fault frequency of the machine. Lastly, the knowledge base was built based on the field rules summarized by experts to identify the detailed fault type. Results have shown that SVM is a powerful tool to accomplish gearbox fault pattern recognition when the sample size is small, whereas the wavelet lifting scheme can effectively extract fault features, and rule-based reasoning can be used to identify the detailed fault type. Therefore, a method that combines SVM, wavelet lifting and rule-based reasoning ensures effective gearbox fault diagnosis. |
format | Online Article Text |
id | pubmed-3292134 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | Molecular Diversity Preservation International (MDPI) |
record_format | MEDLINE/PubMed |
spelling | pubmed-32921342012-03-07 Intelligent Gearbox Diagnosis Methods Based on SVM, Wavelet Lifting and RBR Gao, Lixin Ren, Zhiqiang Tang, Wenliang Wang, Huaqing Chen, Peng Sensors (Basel) Communication Given the problems in intelligent gearbox diagnosis methods, it is difficult to obtain the desired information and a large enough sample size to study; therefore, we propose the application of various methods for gearbox fault diagnosis, including wavelet lifting, a support vector machine (SVM) and rule-based reasoning (RBR). In a complex field environment, it is less likely for machines to have the same fault; moreover, the fault features can also vary. Therefore, a SVM could be used for the initial diagnosis. First, gearbox vibration signals were processed with wavelet packet decomposition, and the signal energy coefficients of each frequency band were extracted and used as input feature vectors in SVM for normal and faulty pattern recognition. Second, precision analysis using wavelet lifting could successfully filter out the noisy signals while maintaining the impulse characteristics of the fault; thus effectively extracting the fault frequency of the machine. Lastly, the knowledge base was built based on the field rules summarized by experts to identify the detailed fault type. Results have shown that SVM is a powerful tool to accomplish gearbox fault pattern recognition when the sample size is small, whereas the wavelet lifting scheme can effectively extract fault features, and rule-based reasoning can be used to identify the detailed fault type. Therefore, a method that combines SVM, wavelet lifting and rule-based reasoning ensures effective gearbox fault diagnosis. Molecular Diversity Preservation International (MDPI) 2010-05-04 /pmc/articles/PMC3292134/ /pubmed/22399894 http://dx.doi.org/10.3390/s100504602 Text en © 2010 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 license (http://creativecommons.org/licenses/by/3.0/). |
spellingShingle | Communication Gao, Lixin Ren, Zhiqiang Tang, Wenliang Wang, Huaqing Chen, Peng Intelligent Gearbox Diagnosis Methods Based on SVM, Wavelet Lifting and RBR |
title | Intelligent Gearbox Diagnosis Methods Based on SVM, Wavelet Lifting and RBR |
title_full | Intelligent Gearbox Diagnosis Methods Based on SVM, Wavelet Lifting and RBR |
title_fullStr | Intelligent Gearbox Diagnosis Methods Based on SVM, Wavelet Lifting and RBR |
title_full_unstemmed | Intelligent Gearbox Diagnosis Methods Based on SVM, Wavelet Lifting and RBR |
title_short | Intelligent Gearbox Diagnosis Methods Based on SVM, Wavelet Lifting and RBR |
title_sort | intelligent gearbox diagnosis methods based on svm, wavelet lifting and rbr |
topic | Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3292134/ https://www.ncbi.nlm.nih.gov/pubmed/22399894 http://dx.doi.org/10.3390/s100504602 |
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