Statistical reliability assessment for small sample of failure data of dumper diesel engines based on power law process and maximum likelihood estimation
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/1822/74232 |
Resumo: | Dumpers or dump trucks are used all over the world to move overburden from many opencast mines. Diesel engines are the main driving force behind the trucks. The frequency of damage due to the failure of diesel engines is enormous. Therefore, efforts are necessary to analyze failure to reduce the downtime periods. A detailed analysis of engine failure at the subsystem level needs to be done. Reliability analysis and maintenance planning remain the norm in this regard. The obstacle faced while analysing the reliability of dumpers was the availability of a large number of data failures. In this paper, this issue is addressed by using Common Beta Hypothesis test and Meta-analysis test. The engine is divided into five subsystems. The result shows that all five subsystems pass the CBH test and Meta-analysis test. Accordingly, the failure data is grouped. The trend test of grouped failure data shows that the Failure data of two subsystems follows the independent and identically distributed characteristics while the remaining three do not follow it. The reliability is estimated for all five subsystems. Finally, fuel supply subsystems show the highest reliability while the lowest value is seen for self-starting subsystems. |
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Statistical reliability assessment for small sample of failure data of dumper diesel engines based on power law process and maximum likelihood estimationTime between failure (TBF)Common beta hypothesis (CBH) testMeta-analysisLevel of heterogeneityReliabilityMean time between failure (MTBF)Science & TechnologyDumpers or dump trucks are used all over the world to move overburden from many opencast mines. Diesel engines are the main driving force behind the trucks. The frequency of damage due to the failure of diesel engines is enormous. Therefore, efforts are necessary to analyze failure to reduce the downtime periods. A detailed analysis of engine failure at the subsystem level needs to be done. Reliability analysis and maintenance planning remain the norm in this regard. The obstacle faced while analysing the reliability of dumpers was the availability of a large number of data failures. In this paper, this issue is addressed by using Common Beta Hypothesis test and Meta-analysis test. The engine is divided into five subsystems. The result shows that all five subsystems pass the CBH test and Meta-analysis test. Accordingly, the failure data is grouped. The trend test of grouped failure data shows that the Failure data of two subsystems follows the independent and identically distributed characteristics while the remaining three do not follow it. The reliability is estimated for all five subsystems. Finally, fuel supply subsystems show the highest reliability while the lowest value is seen for self-starting subsystems.The authors are grateful to FCT—Fundação para a Ciência e Tecnologia who financially supported this work through the RD Units Project Scope: UIDP/04077/2020 and UIDB/04077/2020.Multidisciplinary Digital Publishing Institute (MDPI)Universidade do MinhoDinkar, Brajeshkumar KishorilalMukhopadhyay, Alok KumarChattopadhyaya, SomnathSharma, ShubhamAlam, FirozMachado, José2021-062021-06-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/74232engDinkar, B.K.; Mukhopadhyay, A.K.; Chattopadhyaya, S.; Sharma, S.; Alam, F.; Machado, J. Statistical Reliability Assessment for Small Sample of Failure Data of Dumper Diesel Engines Based on Power Law Process and Maximum Likelihood Estimation. Appl. Sci. 2021, 11, 5387. https://doi.org/10.3390/app111253872076-341710.3390/app11125387https://www.mdpi.com/2076-3417/11/12/5387info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:44:44Zoai:repositorium.sdum.uminho.pt:1822/74232Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:42:28.731167Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Statistical reliability assessment for small sample of failure data of dumper diesel engines based on power law process and maximum likelihood estimation |
title |
Statistical reliability assessment for small sample of failure data of dumper diesel engines based on power law process and maximum likelihood estimation |
spellingShingle |
Statistical reliability assessment for small sample of failure data of dumper diesel engines based on power law process and maximum likelihood estimation Dinkar, Brajeshkumar Kishorilal Time between failure (TBF) Common beta hypothesis (CBH) test Meta-analysis Level of heterogeneity Reliability Mean time between failure (MTBF) Science & Technology |
title_short |
Statistical reliability assessment for small sample of failure data of dumper diesel engines based on power law process and maximum likelihood estimation |
title_full |
Statistical reliability assessment for small sample of failure data of dumper diesel engines based on power law process and maximum likelihood estimation |
title_fullStr |
Statistical reliability assessment for small sample of failure data of dumper diesel engines based on power law process and maximum likelihood estimation |
title_full_unstemmed |
Statistical reliability assessment for small sample of failure data of dumper diesel engines based on power law process and maximum likelihood estimation |
title_sort |
Statistical reliability assessment for small sample of failure data of dumper diesel engines based on power law process and maximum likelihood estimation |
author |
Dinkar, Brajeshkumar Kishorilal |
author_facet |
Dinkar, Brajeshkumar Kishorilal Mukhopadhyay, Alok Kumar Chattopadhyaya, Somnath Sharma, Shubham Alam, Firoz Machado, José |
author_role |
author |
author2 |
Mukhopadhyay, Alok Kumar Chattopadhyaya, Somnath Sharma, Shubham Alam, Firoz Machado, José |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Dinkar, Brajeshkumar Kishorilal Mukhopadhyay, Alok Kumar Chattopadhyaya, Somnath Sharma, Shubham Alam, Firoz Machado, José |
dc.subject.por.fl_str_mv |
Time between failure (TBF) Common beta hypothesis (CBH) test Meta-analysis Level of heterogeneity Reliability Mean time between failure (MTBF) Science & Technology |
topic |
Time between failure (TBF) Common beta hypothesis (CBH) test Meta-analysis Level of heterogeneity Reliability Mean time between failure (MTBF) Science & Technology |
description |
Dumpers or dump trucks are used all over the world to move overburden from many opencast mines. Diesel engines are the main driving force behind the trucks. The frequency of damage due to the failure of diesel engines is enormous. Therefore, efforts are necessary to analyze failure to reduce the downtime periods. A detailed analysis of engine failure at the subsystem level needs to be done. Reliability analysis and maintenance planning remain the norm in this regard. The obstacle faced while analysing the reliability of dumpers was the availability of a large number of data failures. In this paper, this issue is addressed by using Common Beta Hypothesis test and Meta-analysis test. The engine is divided into five subsystems. The result shows that all five subsystems pass the CBH test and Meta-analysis test. Accordingly, the failure data is grouped. The trend test of grouped failure data shows that the Failure data of two subsystems follows the independent and identically distributed characteristics while the remaining three do not follow it. The reliability is estimated for all five subsystems. Finally, fuel supply subsystems show the highest reliability while the lowest value is seen for self-starting subsystems. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-06 2021-06-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/1822/74232 |
url |
http://hdl.handle.net/1822/74232 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Dinkar, B.K.; Mukhopadhyay, A.K.; Chattopadhyaya, S.; Sharma, S.; Alam, F.; Machado, J. Statistical Reliability Assessment for Small Sample of Failure Data of Dumper Diesel Engines Based on Power Law Process and Maximum Likelihood Estimation. Appl. Sci. 2021, 11, 5387. https://doi.org/10.3390/app11125387 2076-3417 10.3390/app11125387 https://www.mdpi.com/2076-3417/11/12/5387 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Multidisciplinary Digital Publishing Institute (MDPI) |
publisher.none.fl_str_mv |
Multidisciplinary Digital Publishing Institute (MDPI) |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799132977468276736 |