A mixture frailty model for maintainability analysis of mechanical components: 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...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zaki, Rezgar [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Anmerkung: |
© The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2019 |
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| Übergeordnetes Werk: |
Enthalten in: International Journal of Systems Assurance Engineering and Management - Springer-Verlag, 2010, 10(2019), 6 vom: 07. Nov., Seite 1646-1653 |
|---|---|
| Übergeordnetes Werk: |
volume:10 ; year:2019 ; number:6 ; day:07 ; month:11 ; pages:1646-1653 |
| Links: |
|---|
| DOI / URN: |
10.1007/s13198-019-00917-3 |
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SPR03130124X |
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| 520 | |a 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. | ||
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10.1007/s13198-019-00917-3 doi (DE-627)SPR03130124X (SPR)s13198-019-00917-3-e DE-627 ger DE-627 rakwb eng Zaki, Rezgar verfasserin aut A mixture frailty model for maintainability analysis of mechanical components: a case study 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2019 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. Mixture Weibull (dpeaa)DE-He213 Failure model (dpeaa)DE-He213 Repair process (dpeaa)DE-He213 Covariates (dpeaa)DE-He213 Repair time (dpeaa)DE-He213 Maintainability (dpeaa)DE-He213 Frailty model (dpeaa)DE-He213 Barabadi, Abbas (orcid)0000-0001-8514-9557 aut Qarahasanlou, Ali Nouri aut Garmabaki, A. H. S. aut Enthalten in International Journal of Systems Assurance Engineering and Management Springer-Verlag, 2010 10(2019), 6 vom: 07. Nov., Seite 1646-1653 (DE-627)SPR031222420 nnns volume:10 year:2019 number:6 day:07 month:11 pages:1646-1653 https://dx.doi.org/10.1007/s13198-019-00917-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 10 2019 6 07 11 1646-1653 |
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10.1007/s13198-019-00917-3 doi (DE-627)SPR03130124X (SPR)s13198-019-00917-3-e DE-627 ger DE-627 rakwb eng Zaki, Rezgar verfasserin aut A mixture frailty model for maintainability analysis of mechanical components: a case study 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2019 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. Mixture Weibull (dpeaa)DE-He213 Failure model (dpeaa)DE-He213 Repair process (dpeaa)DE-He213 Covariates (dpeaa)DE-He213 Repair time (dpeaa)DE-He213 Maintainability (dpeaa)DE-He213 Frailty model (dpeaa)DE-He213 Barabadi, Abbas (orcid)0000-0001-8514-9557 aut Qarahasanlou, Ali Nouri aut Garmabaki, A. H. S. aut Enthalten in International Journal of Systems Assurance Engineering and Management Springer-Verlag, 2010 10(2019), 6 vom: 07. Nov., Seite 1646-1653 (DE-627)SPR031222420 nnns volume:10 year:2019 number:6 day:07 month:11 pages:1646-1653 https://dx.doi.org/10.1007/s13198-019-00917-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 10 2019 6 07 11 1646-1653 |
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10.1007/s13198-019-00917-3 doi (DE-627)SPR03130124X (SPR)s13198-019-00917-3-e DE-627 ger DE-627 rakwb eng Zaki, Rezgar verfasserin aut A mixture frailty model for maintainability analysis of mechanical components: a case study 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2019 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. Mixture Weibull (dpeaa)DE-He213 Failure model (dpeaa)DE-He213 Repair process (dpeaa)DE-He213 Covariates (dpeaa)DE-He213 Repair time (dpeaa)DE-He213 Maintainability (dpeaa)DE-He213 Frailty model (dpeaa)DE-He213 Barabadi, Abbas (orcid)0000-0001-8514-9557 aut Qarahasanlou, Ali Nouri aut Garmabaki, A. H. S. aut Enthalten in International Journal of Systems Assurance Engineering and Management Springer-Verlag, 2010 10(2019), 6 vom: 07. Nov., Seite 1646-1653 (DE-627)SPR031222420 nnns volume:10 year:2019 number:6 day:07 month:11 pages:1646-1653 https://dx.doi.org/10.1007/s13198-019-00917-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 10 2019 6 07 11 1646-1653 |
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10.1007/s13198-019-00917-3 doi (DE-627)SPR03130124X (SPR)s13198-019-00917-3-e DE-627 ger DE-627 rakwb eng Zaki, Rezgar verfasserin aut A mixture frailty model for maintainability analysis of mechanical components: a case study 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2019 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. Mixture Weibull (dpeaa)DE-He213 Failure model (dpeaa)DE-He213 Repair process (dpeaa)DE-He213 Covariates (dpeaa)DE-He213 Repair time (dpeaa)DE-He213 Maintainability (dpeaa)DE-He213 Frailty model (dpeaa)DE-He213 Barabadi, Abbas (orcid)0000-0001-8514-9557 aut Qarahasanlou, Ali Nouri aut Garmabaki, A. H. S. aut Enthalten in International Journal of Systems Assurance Engineering and Management Springer-Verlag, 2010 10(2019), 6 vom: 07. Nov., Seite 1646-1653 (DE-627)SPR031222420 nnns volume:10 year:2019 number:6 day:07 month:11 pages:1646-1653 https://dx.doi.org/10.1007/s13198-019-00917-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 10 2019 6 07 11 1646-1653 |
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10.1007/s13198-019-00917-3 doi (DE-627)SPR03130124X (SPR)s13198-019-00917-3-e DE-627 ger DE-627 rakwb eng Zaki, Rezgar verfasserin aut A mixture frailty model for maintainability analysis of mechanical components: a case study 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2019 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. Mixture Weibull (dpeaa)DE-He213 Failure model (dpeaa)DE-He213 Repair process (dpeaa)DE-He213 Covariates (dpeaa)DE-He213 Repair time (dpeaa)DE-He213 Maintainability (dpeaa)DE-He213 Frailty model (dpeaa)DE-He213 Barabadi, Abbas (orcid)0000-0001-8514-9557 aut Qarahasanlou, Ali Nouri aut Garmabaki, A. H. S. aut Enthalten in International Journal of Systems Assurance Engineering and Management Springer-Verlag, 2010 10(2019), 6 vom: 07. Nov., Seite 1646-1653 (DE-627)SPR031222420 nnns volume:10 year:2019 number:6 day:07 month:11 pages:1646-1653 https://dx.doi.org/10.1007/s13198-019-00917-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 10 2019 6 07 11 1646-1653 |
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A mixture frailty model for maintainability analysis of mechanical components: a case study Mixture Weibull (dpeaa)DE-He213 Failure model (dpeaa)DE-He213 Repair process (dpeaa)DE-He213 Covariates (dpeaa)DE-He213 Repair time (dpeaa)DE-He213 Maintainability (dpeaa)DE-He213 Frailty model (dpeaa)DE-He213 |
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A mixture frailty model for maintainability analysis of mechanical components: a case study |
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A mixture frailty model for maintainability analysis of mechanical components: a case study |
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Zaki, Rezgar |
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Zaki, Rezgar Barabadi, Abbas Qarahasanlou, Ali Nouri Garmabaki, A. H. S. |
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Elektronische Aufsätze |
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mixture frailty model for maintainability analysis of mechanical components: a case study |
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A mixture frailty model for maintainability analysis of mechanical components: a case study |
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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. © The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2019 |
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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. © The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2019 |
| abstract_unstemmed |
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. © The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2019 |
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Barabadi, Abbas Qarahasanlou, Ali Nouri Garmabaki, A. H. S. |
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