Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Gao, Lixin, Ren, Zhiqiang, Tang, Wenliang, Wang, Huaqing, Chen, Peng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2010
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
_version_ 1782225240487624704
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
work_keys_str_mv AT gaolixin intelligentgearboxdiagnosismethodsbasedonsvmwaveletliftingandrbr
AT renzhiqiang intelligentgearboxdiagnosismethodsbasedonsvmwaveletliftingandrbr
AT tangwenliang intelligentgearboxdiagnosismethodsbasedonsvmwaveletliftingandrbr
AT wanghuaqing intelligentgearboxdiagnosismethodsbasedonsvmwaveletliftingandrbr
AT chenpeng intelligentgearboxdiagnosismethodsbasedonsvmwaveletliftingandrbr