Cargando…

Operation Reliability Assessment for Cutting Tools by Applying a Proportional Covariate Model to Condition Monitoring Information

The reliability of cutting tools is critical to machining precision and production efficiency. The conventional statistic-based reliability assessment method aims at providing a general and overall estimation of reliability for a large population of identical units under given and fixed conditions....

Descripción completa

Detalles Bibliográficos
Autores principales: Cai, Gaigai, Chen, Xuefeng, Li, Bing, Chen, Baojia, He, Zhengjia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3545551/
https://www.ncbi.nlm.nih.gov/pubmed/23201980
http://dx.doi.org/10.3390/s121012964
_version_ 1782255915991302144
author Cai, Gaigai
Chen, Xuefeng
Li, Bing
Chen, Baojia
He, Zhengjia
author_facet Cai, Gaigai
Chen, Xuefeng
Li, Bing
Chen, Baojia
He, Zhengjia
author_sort Cai, Gaigai
collection PubMed
description The reliability of cutting tools is critical to machining precision and production efficiency. The conventional statistic-based reliability assessment method aims at providing a general and overall estimation of reliability for a large population of identical units under given and fixed conditions. However, it has limited effectiveness in depicting the operational characteristics of a cutting tool. To overcome this limitation, this paper proposes an approach to assess the operation reliability of cutting tools. A proportional covariate model is introduced to construct the relationship between operation reliability and condition monitoring information. The wavelet packet transform and an improved distance evaluation technique are used to extract sensitive features from vibration signals, and a covariate function is constructed based on the proportional covariate model. Ultimately, the failure rate function of the cutting tool being assessed is calculated using the baseline covariate function obtained from a small sample of historical data. Experimental results and a comparative study show that the proposed method is effective for assessing the operation reliability of cutting tools.
format Online
Article
Text
id pubmed-3545551
institution National Center for Biotechnology Information
language English
publishDate 2012
publisher Molecular Diversity Preservation International (MDPI)
record_format MEDLINE/PubMed
spelling pubmed-35455512013-01-23 Operation Reliability Assessment for Cutting Tools by Applying a Proportional Covariate Model to Condition Monitoring Information Cai, Gaigai Chen, Xuefeng Li, Bing Chen, Baojia He, Zhengjia Sensors (Basel) Article The reliability of cutting tools is critical to machining precision and production efficiency. The conventional statistic-based reliability assessment method aims at providing a general and overall estimation of reliability for a large population of identical units under given and fixed conditions. However, it has limited effectiveness in depicting the operational characteristics of a cutting tool. To overcome this limitation, this paper proposes an approach to assess the operation reliability of cutting tools. A proportional covariate model is introduced to construct the relationship between operation reliability and condition monitoring information. The wavelet packet transform and an improved distance evaluation technique are used to extract sensitive features from vibration signals, and a covariate function is constructed based on the proportional covariate model. Ultimately, the failure rate function of the cutting tool being assessed is calculated using the baseline covariate function obtained from a small sample of historical data. Experimental results and a comparative study show that the proposed method is effective for assessing the operation reliability of cutting tools. Molecular Diversity Preservation International (MDPI) 2012-09-25 /pmc/articles/PMC3545551/ /pubmed/23201980 http://dx.doi.org/10.3390/s121012964 Text en © 2012 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 Article
Cai, Gaigai
Chen, Xuefeng
Li, Bing
Chen, Baojia
He, Zhengjia
Operation Reliability Assessment for Cutting Tools by Applying a Proportional Covariate Model to Condition Monitoring Information
title Operation Reliability Assessment for Cutting Tools by Applying a Proportional Covariate Model to Condition Monitoring Information
title_full Operation Reliability Assessment for Cutting Tools by Applying a Proportional Covariate Model to Condition Monitoring Information
title_fullStr Operation Reliability Assessment for Cutting Tools by Applying a Proportional Covariate Model to Condition Monitoring Information
title_full_unstemmed Operation Reliability Assessment for Cutting Tools by Applying a Proportional Covariate Model to Condition Monitoring Information
title_short Operation Reliability Assessment for Cutting Tools by Applying a Proportional Covariate Model to Condition Monitoring Information
title_sort operation reliability assessment for cutting tools by applying a proportional covariate model to condition monitoring information
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3545551/
https://www.ncbi.nlm.nih.gov/pubmed/23201980
http://dx.doi.org/10.3390/s121012964
work_keys_str_mv AT caigaigai operationreliabilityassessmentforcuttingtoolsbyapplyingaproportionalcovariatemodeltoconditionmonitoringinformation
AT chenxuefeng operationreliabilityassessmentforcuttingtoolsbyapplyingaproportionalcovariatemodeltoconditionmonitoringinformation
AT libing operationreliabilityassessmentforcuttingtoolsbyapplyingaproportionalcovariatemodeltoconditionmonitoringinformation
AT chenbaojia operationreliabilityassessmentforcuttingtoolsbyapplyingaproportionalcovariatemodeltoconditionmonitoringinformation
AT hezhengjia operationreliabilityassessmentforcuttingtoolsbyapplyingaproportionalcovariatemodeltoconditionmonitoringinformation