A mixture frailty model for maintainability analysis of mechanical components
a case study
Document identifier: oai:DiVA.org:ltu-76820
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10.1007/s13198-019-00917-3Keyword: Engineering and Technology,
Civil Engineering,
Other Civil Engineering,
Teknik och teknologier,
Samhällsbyggnadsteknik,
Annan samhällsbyggnadsteknik,
Mixture Weibull,
Failure model,
Repair process,
Covariates,
Repair time,
Maintainability,
Frailty model,
Drift och underhållsteknik,
Operation and MaintenancePublication year: 2019Relevant Sustainable Development Goals (SDGs):
The SDG label(s) above have been assigned by OSDG.aiAbstract: Knowing the maintainability of a component or a system means that repair resource allocations, such as spare part procurement and maintenance training, can be planned and optimized more effectively. Repair data are often collected from multiple and distributed units in different operational conditions, which can introduce heterogeneity into the data. Part of such heterogeneity can be explained and isolated by the observable covariates, whose values and the way that they can affect the item’s maintainability are known. However, some factors which may affect maintainability are typically unknown (unobserved covariates), leading to unobserved heterogeneity. Nevertheless, many researchers have ignored the effect of observed and un-observed covariates, and this may lead to erroneous model selection, as well as wrong conclusions and decisions. Moreover, many authors have simplified their analysis by considering a complex system as a single item. In these studies, the assumption is that all repair data represent an identical repair process for the item. In practice, mechanical systems are composed of multiple parts, with various failure mechanisms, which need different repair processes (repair modes) to return to the operational phase; classical distribution, such as lognormal, which is only a function of time, may not be able to model such complexity. The paper utilizes the mixture frailty model (MFM) in the presence of some specific observed or unobserved covariates to predict maintainability more precisely. MFMs can model the effect of observed and unobserved covariates, as well as identifying different repair processes in the repair dataset. The application of the proposed model is demonstrated by a case study.
Authors
Rezgar Zaki
Department of Technology and Safety, UiT The Arctic University of Norway, Tromsø, Norway
Other publications
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Abbas Barabadi
Department of Technology and Safety, UiT The Arctic University of Norway, Tromsø, Norway
Other publications
>>
Ali Nouri Qarahasanlou
Faculty of Mining Engineering, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran
Other publications
>>
Amir Soleimani Garmabaki
Luleå tekniska universitet; Drift, underhåll och akustik
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>>
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header:
identifier: oai:DiVA.org:ltu-76820
datestamp: 2021-04-19T12:50:51Z
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recordCreationDate: 2019-11-22
identifier:
http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-76820
10.1007/s13198-019-00917-3
2-s2.0-85074858385
titleInfo:
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lang: eng
title: A mixture frailty model for maintainability analysis of mechanical components
subTitle: a case study
abstract: Knowing the maintainability of a component or a system means that repair resource allocations such as spare part procurement and maintenance training can be planned and optimized more effectively. Repair data are often collected from multiple and distributed units in different operational conditions which can introduce heterogeneity into the data. Part of such heterogeneity can be explained and isolated by the observable covariates whose values and the way that they can affect the item’s maintainability are known. However some factors which may affect maintainability are typically unknown (unobserved covariates) leading to unobserved heterogeneity. Nevertheless many researchers have ignored the effect of observed and un-observed covariates and this may lead to erroneous model selection as well as wrong conclusions and decisions. Moreover many authors have simplified their analysis by considering a complex system as a single item. In these studies the assumption is that all repair data represent an identical repair process for the item. In practice mechanical systems are composed of multiple parts with various failure mechanisms which need different repair processes (repair modes) to return to the operational phase; classical distribution such as lognormal which is only a function of time may not be able to model such complexity. The paper utilizes the mixture frailty model (MFM) in the presence of some specific observed or unobserved covariates to predict maintainability more precisely. MFMs can model the effect of observed and unobserved covariates as well as identifying different repair processes in the repair dataset. The application of the proposed model is demonstrated by a case study.
subject:
@attributes:
lang: eng
authority: uka.se
topic:
Engineering and Technology
Civil Engineering
Other Civil Engineering
@attributes:
lang: swe
authority: uka.se
topic:
Teknik och teknologier
Samhällsbyggnadsteknik
Annan samhällsbyggnadsteknik
@attributes:
lang: eng
topic: Mixture Weibull
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lang: eng
topic: Failure model
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lang: eng
topic: Repair process
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lang: eng
topic: Covariates
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lang: eng
topic: Repair time
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lang: eng
topic: Maintainability
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lang: eng
topic: Frailty model
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lang: swe
authority: ltu
topic: Drift och underhållsteknik
genre: Research subject
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lang: eng
authority: ltu
topic: Operation and Maintenance
genre: Research subject
language:
languageTerm: eng
genre:
publication/journal-article
ref
note:
Published
4
Validerad;2020;Nivå 2;2020-01-27 (johcin)
name:
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type: personal
namePart:
Zaki
Rezgar
role:
roleTerm: aut
affiliation: Department of Technology and Safety UiT The Arctic University of Norway Tromsø Norway
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Barabadi
Abbas
role:
roleTerm: aut
affiliation: Department of Technology and Safety UiT The Arctic University of Norway Tromsø Norway
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Qarahasanlou
Ali Nouri
role:
roleTerm: aut
affiliation: Faculty of Mining Engineering Petroleum and Geophysics Shahrood University of Technology Shahrood Iran
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authority: ltu
namePart:
Garmabaki
Amir Soleimani
role:
roleTerm: aut
affiliation:
Luleå tekniska universitet
Drift underhåll och akustik
nameIdentifier:
amigar
0000-0003-2976-5229
originInfo:
dateIssued: 2019
publisher: Springer
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titleInfo:
title: International Journal of Systems Assurance Engineering and Management
identifier:
0975-6809
0976-4348
part:
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type: volume
number: 10
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start: 1646
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