How heterogeneity influences condition-based maintenance for gamma degradation process
In many applications, units from the same population exhibit heterogeneity that they degrade with different rates due to random factors. This article studies how this heterogeneity in degradation influences condition-based maintenance (CBM) policy. Many CBM polices are developed based on gamma proce...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhang, Linmiao [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Rechteinformationen: |
Nutzungsrecht: © 2016 Informa UK Limited, trading as Taylor & Francis Group 2016 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: International journal of production research - London : Taylor & Francis, 1961, 54(2016), 19, Seite 5829-5841 |
|---|---|
| Übergeordnetes Werk: |
volume:54 ; year:2016 ; number:19 ; pages:5829-5841 |
| Links: |
|---|
| DOI / URN: |
10.1080/00207543.2016.1181282 |
|---|
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OLC1981318380 |
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| 520 | |a In many applications, units from the same population exhibit heterogeneity that they degrade with different rates due to random factors. This article studies how this heterogeneity in degradation influences condition-based maintenance (CBM) policy. Many CBM polices are developed based on gamma process because it is popularly used to characterise monotone degradation processes. In this study, we also model the unit's degradation by gamma process. To account for the heterogeneity among units' degradation, we incorporate a random effect parameter in the gamma process. Then the optimal policy for CBM is obtained through Markov decision process. We show that when heterogeneity exists, the transition probability of degradation state depends on both unit's age and observed degradation level. And consequently, the optimal maintenance policy is a monotone control limit policy. We conduct extensive numerical experiments to validate and demonstrate our findings in depth. | ||
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10.1080/00207543.2016.1181282 doi PQ20161012 (DE-627)OLC1981318380 (DE-599)GBVOLC1981318380 (PRQ)c1542-cc246f7df4aae813cefc3b1d8542f2812444aea55a94261f17372b82ed0184290 (KEY)0019873020160000054001905829howheterogeneityinfluencesconditionbasedmaintenanc DE-627 ger DE-627 rakwb eng 600 620 330 DNB 85.35 bkl 52.70 bkl Zhang, Linmiao verfasserin aut How heterogeneity influences condition-based maintenance for gamma degradation process 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In many applications, units from the same population exhibit heterogeneity that they degrade with different rates due to random factors. This article studies how this heterogeneity in degradation influences condition-based maintenance (CBM) policy. Many CBM polices are developed based on gamma process because it is popularly used to characterise monotone degradation processes. In this study, we also model the unit's degradation by gamma process. To account for the heterogeneity among units' degradation, we incorporate a random effect parameter in the gamma process. Then the optimal policy for CBM is obtained through Markov decision process. We show that when heterogeneity exists, the transition probability of degradation state depends on both unit's age and observed degradation level. And consequently, the optimal maintenance policy is a monotone control limit policy. We conduct extensive numerical experiments to validate and demonstrate our findings in depth. Nutzungsrecht: © 2016 Informa UK Limited, trading as Taylor & Francis Group 2016 Markov decision process condition-based maintenance heterogeneity gamma process Markov analysis Repair & maintenance Maintenance management Lei, Yong oth Shen, Houcai oth Enthalten in International journal of production research London : Taylor & Francis, 1961 54(2016), 19, Seite 5829-5841 (DE-627)129358835 (DE-600)160477-6 (DE-576)014731150 0020-7543 nnns volume:54 year:2016 number:19 pages:5829-5841 http://dx.doi.org/10.1080/00207543.2016.1181282 Volltext http://www.tandfonline.com/doi/abs/10.1080/00207543.2016.1181282 http://search.proquest.com/docview/1808758335 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-WIW GBV_ILN_21 GBV_ILN_26 GBV_ILN_70 GBV_ILN_4126 85.35 AVZ 52.70 AVZ AR 54 2016 19 5829-5841 |
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10.1080/00207543.2016.1181282 doi PQ20161012 (DE-627)OLC1981318380 (DE-599)GBVOLC1981318380 (PRQ)c1542-cc246f7df4aae813cefc3b1d8542f2812444aea55a94261f17372b82ed0184290 (KEY)0019873020160000054001905829howheterogeneityinfluencesconditionbasedmaintenanc DE-627 ger DE-627 rakwb eng 600 620 330 DNB 85.35 bkl 52.70 bkl Zhang, Linmiao verfasserin aut How heterogeneity influences condition-based maintenance for gamma degradation process 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In many applications, units from the same population exhibit heterogeneity that they degrade with different rates due to random factors. This article studies how this heterogeneity in degradation influences condition-based maintenance (CBM) policy. Many CBM polices are developed based on gamma process because it is popularly used to characterise monotone degradation processes. In this study, we also model the unit's degradation by gamma process. To account for the heterogeneity among units' degradation, we incorporate a random effect parameter in the gamma process. Then the optimal policy for CBM is obtained through Markov decision process. We show that when heterogeneity exists, the transition probability of degradation state depends on both unit's age and observed degradation level. And consequently, the optimal maintenance policy is a monotone control limit policy. We conduct extensive numerical experiments to validate and demonstrate our findings in depth. Nutzungsrecht: © 2016 Informa UK Limited, trading as Taylor & Francis Group 2016 Markov decision process condition-based maintenance heterogeneity gamma process Markov analysis Repair & maintenance Maintenance management Lei, Yong oth Shen, Houcai oth Enthalten in International journal of production research London : Taylor & Francis, 1961 54(2016), 19, Seite 5829-5841 (DE-627)129358835 (DE-600)160477-6 (DE-576)014731150 0020-7543 nnns volume:54 year:2016 number:19 pages:5829-5841 http://dx.doi.org/10.1080/00207543.2016.1181282 Volltext http://www.tandfonline.com/doi/abs/10.1080/00207543.2016.1181282 http://search.proquest.com/docview/1808758335 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-WIW GBV_ILN_21 GBV_ILN_26 GBV_ILN_70 GBV_ILN_4126 85.35 AVZ 52.70 AVZ AR 54 2016 19 5829-5841 |
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10.1080/00207543.2016.1181282 doi PQ20161012 (DE-627)OLC1981318380 (DE-599)GBVOLC1981318380 (PRQ)c1542-cc246f7df4aae813cefc3b1d8542f2812444aea55a94261f17372b82ed0184290 (KEY)0019873020160000054001905829howheterogeneityinfluencesconditionbasedmaintenanc DE-627 ger DE-627 rakwb eng 600 620 330 DNB 85.35 bkl 52.70 bkl Zhang, Linmiao verfasserin aut How heterogeneity influences condition-based maintenance for gamma degradation process 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In many applications, units from the same population exhibit heterogeneity that they degrade with different rates due to random factors. This article studies how this heterogeneity in degradation influences condition-based maintenance (CBM) policy. Many CBM polices are developed based on gamma process because it is popularly used to characterise monotone degradation processes. In this study, we also model the unit's degradation by gamma process. To account for the heterogeneity among units' degradation, we incorporate a random effect parameter in the gamma process. Then the optimal policy for CBM is obtained through Markov decision process. We show that when heterogeneity exists, the transition probability of degradation state depends on both unit's age and observed degradation level. And consequently, the optimal maintenance policy is a monotone control limit policy. We conduct extensive numerical experiments to validate and demonstrate our findings in depth. Nutzungsrecht: © 2016 Informa UK Limited, trading as Taylor & Francis Group 2016 Markov decision process condition-based maintenance heterogeneity gamma process Markov analysis Repair & maintenance Maintenance management Lei, Yong oth Shen, Houcai oth Enthalten in International journal of production research London : Taylor & Francis, 1961 54(2016), 19, Seite 5829-5841 (DE-627)129358835 (DE-600)160477-6 (DE-576)014731150 0020-7543 nnns volume:54 year:2016 number:19 pages:5829-5841 http://dx.doi.org/10.1080/00207543.2016.1181282 Volltext http://www.tandfonline.com/doi/abs/10.1080/00207543.2016.1181282 http://search.proquest.com/docview/1808758335 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-WIW GBV_ILN_21 GBV_ILN_26 GBV_ILN_70 GBV_ILN_4126 85.35 AVZ 52.70 AVZ AR 54 2016 19 5829-5841 |
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10.1080/00207543.2016.1181282 doi PQ20161012 (DE-627)OLC1981318380 (DE-599)GBVOLC1981318380 (PRQ)c1542-cc246f7df4aae813cefc3b1d8542f2812444aea55a94261f17372b82ed0184290 (KEY)0019873020160000054001905829howheterogeneityinfluencesconditionbasedmaintenanc DE-627 ger DE-627 rakwb eng 600 620 330 DNB 85.35 bkl 52.70 bkl Zhang, Linmiao verfasserin aut How heterogeneity influences condition-based maintenance for gamma degradation process 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In many applications, units from the same population exhibit heterogeneity that they degrade with different rates due to random factors. This article studies how this heterogeneity in degradation influences condition-based maintenance (CBM) policy. Many CBM polices are developed based on gamma process because it is popularly used to characterise monotone degradation processes. In this study, we also model the unit's degradation by gamma process. To account for the heterogeneity among units' degradation, we incorporate a random effect parameter in the gamma process. Then the optimal policy for CBM is obtained through Markov decision process. We show that when heterogeneity exists, the transition probability of degradation state depends on both unit's age and observed degradation level. And consequently, the optimal maintenance policy is a monotone control limit policy. We conduct extensive numerical experiments to validate and demonstrate our findings in depth. Nutzungsrecht: © 2016 Informa UK Limited, trading as Taylor & Francis Group 2016 Markov decision process condition-based maintenance heterogeneity gamma process Markov analysis Repair & maintenance Maintenance management Lei, Yong oth Shen, Houcai oth Enthalten in International journal of production research London : Taylor & Francis, 1961 54(2016), 19, Seite 5829-5841 (DE-627)129358835 (DE-600)160477-6 (DE-576)014731150 0020-7543 nnns volume:54 year:2016 number:19 pages:5829-5841 http://dx.doi.org/10.1080/00207543.2016.1181282 Volltext http://www.tandfonline.com/doi/abs/10.1080/00207543.2016.1181282 http://search.proquest.com/docview/1808758335 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-WIW GBV_ILN_21 GBV_ILN_26 GBV_ILN_70 GBV_ILN_4126 85.35 AVZ 52.70 AVZ AR 54 2016 19 5829-5841 |
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10.1080/00207543.2016.1181282 doi PQ20161012 (DE-627)OLC1981318380 (DE-599)GBVOLC1981318380 (PRQ)c1542-cc246f7df4aae813cefc3b1d8542f2812444aea55a94261f17372b82ed0184290 (KEY)0019873020160000054001905829howheterogeneityinfluencesconditionbasedmaintenanc DE-627 ger DE-627 rakwb eng 600 620 330 DNB 85.35 bkl 52.70 bkl Zhang, Linmiao verfasserin aut How heterogeneity influences condition-based maintenance for gamma degradation process 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In many applications, units from the same population exhibit heterogeneity that they degrade with different rates due to random factors. This article studies how this heterogeneity in degradation influences condition-based maintenance (CBM) policy. Many CBM polices are developed based on gamma process because it is popularly used to characterise monotone degradation processes. In this study, we also model the unit's degradation by gamma process. To account for the heterogeneity among units' degradation, we incorporate a random effect parameter in the gamma process. Then the optimal policy for CBM is obtained through Markov decision process. We show that when heterogeneity exists, the transition probability of degradation state depends on both unit's age and observed degradation level. And consequently, the optimal maintenance policy is a monotone control limit policy. We conduct extensive numerical experiments to validate and demonstrate our findings in depth. Nutzungsrecht: © 2016 Informa UK Limited, trading as Taylor & Francis Group 2016 Markov decision process condition-based maintenance heterogeneity gamma process Markov analysis Repair & maintenance Maintenance management Lei, Yong oth Shen, Houcai oth Enthalten in International journal of production research London : Taylor & Francis, 1961 54(2016), 19, Seite 5829-5841 (DE-627)129358835 (DE-600)160477-6 (DE-576)014731150 0020-7543 nnns volume:54 year:2016 number:19 pages:5829-5841 http://dx.doi.org/10.1080/00207543.2016.1181282 Volltext http://www.tandfonline.com/doi/abs/10.1080/00207543.2016.1181282 http://search.proquest.com/docview/1808758335 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-WIW GBV_ILN_21 GBV_ILN_26 GBV_ILN_70 GBV_ILN_4126 85.35 AVZ 52.70 AVZ AR 54 2016 19 5829-5841 |
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How heterogeneity influences condition-based maintenance for gamma degradation process |
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How heterogeneity influences condition-based maintenance for gamma degradation process |
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Zhang, Linmiao |
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how heterogeneity influences condition-based maintenance for gamma degradation process |
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How heterogeneity influences condition-based maintenance for gamma degradation process |
| abstract |
In many applications, units from the same population exhibit heterogeneity that they degrade with different rates due to random factors. This article studies how this heterogeneity in degradation influences condition-based maintenance (CBM) policy. Many CBM polices are developed based on gamma process because it is popularly used to characterise monotone degradation processes. In this study, we also model the unit's degradation by gamma process. To account for the heterogeneity among units' degradation, we incorporate a random effect parameter in the gamma process. Then the optimal policy for CBM is obtained through Markov decision process. We show that when heterogeneity exists, the transition probability of degradation state depends on both unit's age and observed degradation level. And consequently, the optimal maintenance policy is a monotone control limit policy. We conduct extensive numerical experiments to validate and demonstrate our findings in depth. |
| abstractGer |
In many applications, units from the same population exhibit heterogeneity that they degrade with different rates due to random factors. This article studies how this heterogeneity in degradation influences condition-based maintenance (CBM) policy. Many CBM polices are developed based on gamma process because it is popularly used to characterise monotone degradation processes. In this study, we also model the unit's degradation by gamma process. To account for the heterogeneity among units' degradation, we incorporate a random effect parameter in the gamma process. Then the optimal policy for CBM is obtained through Markov decision process. We show that when heterogeneity exists, the transition probability of degradation state depends on both unit's age and observed degradation level. And consequently, the optimal maintenance policy is a monotone control limit policy. We conduct extensive numerical experiments to validate and demonstrate our findings in depth. |
| abstract_unstemmed |
In many applications, units from the same population exhibit heterogeneity that they degrade with different rates due to random factors. This article studies how this heterogeneity in degradation influences condition-based maintenance (CBM) policy. Many CBM polices are developed based on gamma process because it is popularly used to characterise monotone degradation processes. In this study, we also model the unit's degradation by gamma process. To account for the heterogeneity among units' degradation, we incorporate a random effect parameter in the gamma process. Then the optimal policy for CBM is obtained through Markov decision process. We show that when heterogeneity exists, the transition probability of degradation state depends on both unit's age and observed degradation level. And consequently, the optimal maintenance policy is a monotone control limit policy. We conduct extensive numerical experiments to validate and demonstrate our findings in depth. |
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How heterogeneity influences condition-based maintenance for gamma degradation process |
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