Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
A mixture frailty model for maintainability analysis of mechanical components: a case study
Department of Technology and Safety, UiT The Arctic University of Norway, Tromsø, Norway.
Department of Technology and Safety, UiT The Arctic University of Norway, Tromsø, Norway.
Faculty of Mining Engineering, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0003-2976-5229
2019 (English)In: International Journal of Systems Assurance Engineering and Management, ISSN 0975-6809, E-ISSN 0976-4348Article in journal (Refereed) Epub ahead of print
Abstract [en]

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.

Place, publisher, year, edition, pages
Springer, 2019.
Keywords [en]
Mixture Weibull, Failure model, Repair process, Covariates, Repair time, Maintainability, Frailty model
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-76820DOI: 10.1007/s13198-019-00917-3ISI: 000495019600001OAI: oai:DiVA.org:ltu-76820DiVA, id: diva2:1372223
Available from: 2019-11-22 Created: 2019-11-22 Last updated: 2019-11-22

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Authority records BETA

Garmabaki, Amir Soleimani

Search in DiVA

By author/editor
Garmabaki, Amir Soleimani
By organisation
Operation, Maintenance and Acoustics
In the same journal
International Journal of Systems Assurance Engineering and Management
Other Civil Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 9 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf