Fractional-order optimal control model for the equipment management optimization problem with preventive maintenance
Abstract The current quality status of most machinery and equipment is based on its accumulated historical status, but the influence of the past quality status on the current status of equipment is often overlooked in optimization management. This paper uses a Caputo-type fractional derivative to ch...
Ausführliche Beschreibung
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Gong, Yanping [verfasserIn] |
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Englisch |
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2021 |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - Springer London, 1993, 34(2021), 6 vom: 08. Nov., Seite 4693-4714 |
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volume:34 ; year:2021 ; number:6 ; day:08 ; month:11 ; pages:4693-4714 |
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10.1007/s00521-021-06624-0 |
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OLC2078165573 |
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| 520 | |a Abstract The current quality status of most machinery and equipment is based on its accumulated historical status, but the influence of the past quality status on the current status of equipment is often overlooked in optimization management. This paper uses a Caputo-type fractional derivative to characterize this property. By refining the nature and characteristics of the equipment maintenance effect function and considering the memory characteristics of equipment quality, the existing model is improved, and a fractional-order optimal control model for equipment maintenance and replacement is constructed. Theoretical analyses verify the effectiveness of the fractional-order equipment maintenance management model. Furthermore, the results of numerical experiments also reflect this difference between integer-order and fractional-order equipment maintenance management models. The result shows that with an increase of the order $$\alpha$$, the optimal target value of the equipment maintenance management problem will also increase with the weakening of the memory effect. | ||
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10.1007/s00521-021-06624-0 doi (DE-627)OLC2078165573 (DE-He213)s00521-021-06624-0-p DE-627 ger DE-627 rakwb eng 004 VZ Gong, Yanping verfasserin aut Fractional-order optimal control model for the equipment management optimization problem with preventive maintenance 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract The current quality status of most machinery and equipment is based on its accumulated historical status, but the influence of the past quality status on the current status of equipment is often overlooked in optimization management. This paper uses a Caputo-type fractional derivative to characterize this property. By refining the nature and characteristics of the equipment maintenance effect function and considering the memory characteristics of equipment quality, the existing model is improved, and a fractional-order optimal control model for equipment maintenance and replacement is constructed. Theoretical analyses verify the effectiveness of the fractional-order equipment maintenance management model. Furthermore, the results of numerical experiments also reflect this difference between integer-order and fractional-order equipment maintenance management models. The result shows that with an increase of the order $$\alpha$$, the optimal target value of the equipment maintenance management problem will also increase with the weakening of the memory effect. Equipment management Memory effect Fractional-order model Optimal control Equipment quality function Zha, Mingjiang aut Lv, Zhanmei (orcid)0000-0002-0524-9513 aut Enthalten in Neural computing & applications Springer London, 1993 34(2021), 6 vom: 08. Nov., Seite 4693-4714 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:34 year:2021 number:6 day:08 month:11 pages:4693-4714 https://doi.org/10.1007/s00521-021-06624-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 34 2021 6 08 11 4693-4714 |
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10.1007/s00521-021-06624-0 doi (DE-627)OLC2078165573 (DE-He213)s00521-021-06624-0-p DE-627 ger DE-627 rakwb eng 004 VZ Gong, Yanping verfasserin aut Fractional-order optimal control model for the equipment management optimization problem with preventive maintenance 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract The current quality status of most machinery and equipment is based on its accumulated historical status, but the influence of the past quality status on the current status of equipment is often overlooked in optimization management. This paper uses a Caputo-type fractional derivative to characterize this property. By refining the nature and characteristics of the equipment maintenance effect function and considering the memory characteristics of equipment quality, the existing model is improved, and a fractional-order optimal control model for equipment maintenance and replacement is constructed. Theoretical analyses verify the effectiveness of the fractional-order equipment maintenance management model. Furthermore, the results of numerical experiments also reflect this difference between integer-order and fractional-order equipment maintenance management models. The result shows that with an increase of the order $$\alpha$$, the optimal target value of the equipment maintenance management problem will also increase with the weakening of the memory effect. Equipment management Memory effect Fractional-order model Optimal control Equipment quality function Zha, Mingjiang aut Lv, Zhanmei (orcid)0000-0002-0524-9513 aut Enthalten in Neural computing & applications Springer London, 1993 34(2021), 6 vom: 08. Nov., Seite 4693-4714 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:34 year:2021 number:6 day:08 month:11 pages:4693-4714 https://doi.org/10.1007/s00521-021-06624-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 34 2021 6 08 11 4693-4714 |
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10.1007/s00521-021-06624-0 doi (DE-627)OLC2078165573 (DE-He213)s00521-021-06624-0-p DE-627 ger DE-627 rakwb eng 004 VZ Gong, Yanping verfasserin aut Fractional-order optimal control model for the equipment management optimization problem with preventive maintenance 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract The current quality status of most machinery and equipment is based on its accumulated historical status, but the influence of the past quality status on the current status of equipment is often overlooked in optimization management. This paper uses a Caputo-type fractional derivative to characterize this property. By refining the nature and characteristics of the equipment maintenance effect function and considering the memory characteristics of equipment quality, the existing model is improved, and a fractional-order optimal control model for equipment maintenance and replacement is constructed. Theoretical analyses verify the effectiveness of the fractional-order equipment maintenance management model. Furthermore, the results of numerical experiments also reflect this difference between integer-order and fractional-order equipment maintenance management models. The result shows that with an increase of the order $$\alpha$$, the optimal target value of the equipment maintenance management problem will also increase with the weakening of the memory effect. Equipment management Memory effect Fractional-order model Optimal control Equipment quality function Zha, Mingjiang aut Lv, Zhanmei (orcid)0000-0002-0524-9513 aut Enthalten in Neural computing & applications Springer London, 1993 34(2021), 6 vom: 08. Nov., Seite 4693-4714 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:34 year:2021 number:6 day:08 month:11 pages:4693-4714 https://doi.org/10.1007/s00521-021-06624-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 34 2021 6 08 11 4693-4714 |
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10.1007/s00521-021-06624-0 doi (DE-627)OLC2078165573 (DE-He213)s00521-021-06624-0-p DE-627 ger DE-627 rakwb eng 004 VZ Gong, Yanping verfasserin aut Fractional-order optimal control model for the equipment management optimization problem with preventive maintenance 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract The current quality status of most machinery and equipment is based on its accumulated historical status, but the influence of the past quality status on the current status of equipment is often overlooked in optimization management. This paper uses a Caputo-type fractional derivative to characterize this property. By refining the nature and characteristics of the equipment maintenance effect function and considering the memory characteristics of equipment quality, the existing model is improved, and a fractional-order optimal control model for equipment maintenance and replacement is constructed. Theoretical analyses verify the effectiveness of the fractional-order equipment maintenance management model. Furthermore, the results of numerical experiments also reflect this difference between integer-order and fractional-order equipment maintenance management models. The result shows that with an increase of the order $$\alpha$$, the optimal target value of the equipment maintenance management problem will also increase with the weakening of the memory effect. Equipment management Memory effect Fractional-order model Optimal control Equipment quality function Zha, Mingjiang aut Lv, Zhanmei (orcid)0000-0002-0524-9513 aut Enthalten in Neural computing & applications Springer London, 1993 34(2021), 6 vom: 08. Nov., Seite 4693-4714 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:34 year:2021 number:6 day:08 month:11 pages:4693-4714 https://doi.org/10.1007/s00521-021-06624-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 34 2021 6 08 11 4693-4714 |
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Abstract The current quality status of most machinery and equipment is based on its accumulated historical status, but the influence of the past quality status on the current status of equipment is often overlooked in optimization management. This paper uses a Caputo-type fractional derivative to characterize this property. By refining the nature and characteristics of the equipment maintenance effect function and considering the memory characteristics of equipment quality, the existing model is improved, and a fractional-order optimal control model for equipment maintenance and replacement is constructed. Theoretical analyses verify the effectiveness of the fractional-order equipment maintenance management model. Furthermore, the results of numerical experiments also reflect this difference between integer-order and fractional-order equipment maintenance management models. The result shows that with an increase of the order $$\alpha$$, the optimal target value of the equipment maintenance management problem will also increase with the weakening of the memory effect. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 |
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Abstract The current quality status of most machinery and equipment is based on its accumulated historical status, but the influence of the past quality status on the current status of equipment is often overlooked in optimization management. This paper uses a Caputo-type fractional derivative to characterize this property. By refining the nature and characteristics of the equipment maintenance effect function and considering the memory characteristics of equipment quality, the existing model is improved, and a fractional-order optimal control model for equipment maintenance and replacement is constructed. Theoretical analyses verify the effectiveness of the fractional-order equipment maintenance management model. Furthermore, the results of numerical experiments also reflect this difference between integer-order and fractional-order equipment maintenance management models. The result shows that with an increase of the order $$\alpha$$, the optimal target value of the equipment maintenance management problem will also increase with the weakening of the memory effect. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract The current quality status of most machinery and equipment is based on its accumulated historical status, but the influence of the past quality status on the current status of equipment is often overlooked in optimization management. This paper uses a Caputo-type fractional derivative to characterize this property. By refining the nature and characteristics of the equipment maintenance effect function and considering the memory characteristics of equipment quality, the existing model is improved, and a fractional-order optimal control model for equipment maintenance and replacement is constructed. Theoretical analyses verify the effectiveness of the fractional-order equipment maintenance management model. Furthermore, the results of numerical experiments also reflect this difference between integer-order and fractional-order equipment maintenance management models. The result shows that with an increase of the order $$\alpha$$, the optimal target value of the equipment maintenance management problem will also increase with the weakening of the memory effect. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 |
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Zha, Mingjiang Lv, Zhanmei |
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10.1007/s00521-021-06624-0 |
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This paper uses a Caputo-type fractional derivative to characterize this property. By refining the nature and characteristics of the equipment maintenance effect function and considering the memory characteristics of equipment quality, the existing model is improved, and a fractional-order optimal control model for equipment maintenance and replacement is constructed. Theoretical analyses verify the effectiveness of the fractional-order equipment maintenance management model. Furthermore, the results of numerical experiments also reflect this difference between integer-order and fractional-order equipment maintenance management models. 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