Application of dynamic evidential networks in reliability analysis of complex systems with epistemic uncertainty and multiple life distributions
Abstract With the modernization and intelligent of industrial equipment and systems, the challenges of dynamic characteristics, failure dependency and uncertainties have aroused by the increasing of system complexity. Besides, various types of components may follow different life distributions which...
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| Autor*in: |
Mi, Jinhua [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2019 |
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| Übergeordnetes Werk: |
Enthalten in: Annals of operations research - Springer US, 1984, 311(2019), 1 vom: 10. Apr., Seite 311-333 |
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| Übergeordnetes Werk: |
volume:311 ; year:2019 ; number:1 ; day:10 ; month:04 ; pages:311-333 |
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| DOI / URN: |
10.1007/s10479-019-03211-4 |
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OLC2078229016 |
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| 520 | |a Abstract With the modernization and intelligent of industrial equipment and systems, the challenges of dynamic characteristics, failure dependency and uncertainties have aroused by the increasing of system complexity. Besides, various types of components may follow different life distributions which bring the multiple life distributions problem in systems. In order to model the impact of time dependency and epistemic uncertainty on the failure behavior of system, this paper combines the flexible dynamic modeling with the uncertainty expression. Its advantages are intuitively graphical representation and reasoning that brought by evidential network (EN). After that, the discrete time dynamic evidential network (DT-DEN) is introduced to analyze the reliability of complex systems, and the network inference mechanism is clearly defined. The evidence theory and original definition and inference mechanism of conventional EN is firstly recommended, and the DT-DEN is further presented. Furthermore, the multiple life distributions are synthesized into the DT-DEN to tackle the epistemic uncertainty and mixed life distribution challenges. Specifically, the dynamic logic gates are converted into equivalent DENs with distinguished conditional mass tables, and then the belief interval of system reliability can be calculated by network forward reasoning. Finally, the availability and efficiency of the proposed method is verified by some numerical examples. | ||
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| 650 | 4 | |a Epistemic uncertainty | |
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| 700 | 1 | |a Song, Yufei |4 aut | |
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| 700 | 1 | |a Chen, Kai |4 aut | |
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10.1007/s10479-019-03211-4 doi (DE-627)OLC2078229016 (DE-He213)s10479-019-03211-4-p DE-627 ger DE-627 rakwb eng 004 VZ 3,2 ssgn Mi, Jinhua verfasserin aut Application of dynamic evidential networks in reliability analysis of complex systems with epistemic uncertainty and multiple life distributions 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract With the modernization and intelligent of industrial equipment and systems, the challenges of dynamic characteristics, failure dependency and uncertainties have aroused by the increasing of system complexity. Besides, various types of components may follow different life distributions which bring the multiple life distributions problem in systems. In order to model the impact of time dependency and epistemic uncertainty on the failure behavior of system, this paper combines the flexible dynamic modeling with the uncertainty expression. Its advantages are intuitively graphical representation and reasoning that brought by evidential network (EN). After that, the discrete time dynamic evidential network (DT-DEN) is introduced to analyze the reliability of complex systems, and the network inference mechanism is clearly defined. The evidence theory and original definition and inference mechanism of conventional EN is firstly recommended, and the DT-DEN is further presented. Furthermore, the multiple life distributions are synthesized into the DT-DEN to tackle the epistemic uncertainty and mixed life distribution challenges. Specifically, the dynamic logic gates are converted into equivalent DENs with distinguished conditional mass tables, and then the belief interval of system reliability can be calculated by network forward reasoning. Finally, the availability and efficiency of the proposed method is verified by some numerical examples. Evidence theory Dynamic evidential networks Epistemic uncertainty Multiple life distribution Cheng, Yuhua (orcid)0000-0002-5488-4783 aut Song, Yufei aut Bai, Libing aut Chen, Kai aut Enthalten in Annals of operations research Springer US, 1984 311(2019), 1 vom: 10. Apr., Seite 311-333 (DE-627)12964370X (DE-600)252629-3 (DE-576)018141862 0254-5330 volume:311 year:2019 number:1 day:10 month:04 pages:311-333 https://doi.org/10.1007/s10479-019-03211-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-MAT AR 311 2019 1 10 04 311-333 |
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10.1007/s10479-019-03211-4 doi (DE-627)OLC2078229016 (DE-He213)s10479-019-03211-4-p DE-627 ger DE-627 rakwb eng 004 VZ 3,2 ssgn Mi, Jinhua verfasserin aut Application of dynamic evidential networks in reliability analysis of complex systems with epistemic uncertainty and multiple life distributions 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract With the modernization and intelligent of industrial equipment and systems, the challenges of dynamic characteristics, failure dependency and uncertainties have aroused by the increasing of system complexity. Besides, various types of components may follow different life distributions which bring the multiple life distributions problem in systems. In order to model the impact of time dependency and epistemic uncertainty on the failure behavior of system, this paper combines the flexible dynamic modeling with the uncertainty expression. Its advantages are intuitively graphical representation and reasoning that brought by evidential network (EN). After that, the discrete time dynamic evidential network (DT-DEN) is introduced to analyze the reliability of complex systems, and the network inference mechanism is clearly defined. The evidence theory and original definition and inference mechanism of conventional EN is firstly recommended, and the DT-DEN is further presented. Furthermore, the multiple life distributions are synthesized into the DT-DEN to tackle the epistemic uncertainty and mixed life distribution challenges. Specifically, the dynamic logic gates are converted into equivalent DENs with distinguished conditional mass tables, and then the belief interval of system reliability can be calculated by network forward reasoning. Finally, the availability and efficiency of the proposed method is verified by some numerical examples. Evidence theory Dynamic evidential networks Epistemic uncertainty Multiple life distribution Cheng, Yuhua (orcid)0000-0002-5488-4783 aut Song, Yufei aut Bai, Libing aut Chen, Kai aut Enthalten in Annals of operations research Springer US, 1984 311(2019), 1 vom: 10. Apr., Seite 311-333 (DE-627)12964370X (DE-600)252629-3 (DE-576)018141862 0254-5330 volume:311 year:2019 number:1 day:10 month:04 pages:311-333 https://doi.org/10.1007/s10479-019-03211-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-MAT AR 311 2019 1 10 04 311-333 |
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10.1007/s10479-019-03211-4 doi (DE-627)OLC2078229016 (DE-He213)s10479-019-03211-4-p DE-627 ger DE-627 rakwb eng 004 VZ 3,2 ssgn Mi, Jinhua verfasserin aut Application of dynamic evidential networks in reliability analysis of complex systems with epistemic uncertainty and multiple life distributions 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract With the modernization and intelligent of industrial equipment and systems, the challenges of dynamic characteristics, failure dependency and uncertainties have aroused by the increasing of system complexity. Besides, various types of components may follow different life distributions which bring the multiple life distributions problem in systems. In order to model the impact of time dependency and epistemic uncertainty on the failure behavior of system, this paper combines the flexible dynamic modeling with the uncertainty expression. Its advantages are intuitively graphical representation and reasoning that brought by evidential network (EN). After that, the discrete time dynamic evidential network (DT-DEN) is introduced to analyze the reliability of complex systems, and the network inference mechanism is clearly defined. The evidence theory and original definition and inference mechanism of conventional EN is firstly recommended, and the DT-DEN is further presented. Furthermore, the multiple life distributions are synthesized into the DT-DEN to tackle the epistemic uncertainty and mixed life distribution challenges. Specifically, the dynamic logic gates are converted into equivalent DENs with distinguished conditional mass tables, and then the belief interval of system reliability can be calculated by network forward reasoning. Finally, the availability and efficiency of the proposed method is verified by some numerical examples. Evidence theory Dynamic evidential networks Epistemic uncertainty Multiple life distribution Cheng, Yuhua (orcid)0000-0002-5488-4783 aut Song, Yufei aut Bai, Libing aut Chen, Kai aut Enthalten in Annals of operations research Springer US, 1984 311(2019), 1 vom: 10. Apr., Seite 311-333 (DE-627)12964370X (DE-600)252629-3 (DE-576)018141862 0254-5330 volume:311 year:2019 number:1 day:10 month:04 pages:311-333 https://doi.org/10.1007/s10479-019-03211-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-MAT AR 311 2019 1 10 04 311-333 |
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10.1007/s10479-019-03211-4 doi (DE-627)OLC2078229016 (DE-He213)s10479-019-03211-4-p DE-627 ger DE-627 rakwb eng 004 VZ 3,2 ssgn Mi, Jinhua verfasserin aut Application of dynamic evidential networks in reliability analysis of complex systems with epistemic uncertainty and multiple life distributions 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract With the modernization and intelligent of industrial equipment and systems, the challenges of dynamic characteristics, failure dependency and uncertainties have aroused by the increasing of system complexity. Besides, various types of components may follow different life distributions which bring the multiple life distributions problem in systems. In order to model the impact of time dependency and epistemic uncertainty on the failure behavior of system, this paper combines the flexible dynamic modeling with the uncertainty expression. Its advantages are intuitively graphical representation and reasoning that brought by evidential network (EN). After that, the discrete time dynamic evidential network (DT-DEN) is introduced to analyze the reliability of complex systems, and the network inference mechanism is clearly defined. The evidence theory and original definition and inference mechanism of conventional EN is firstly recommended, and the DT-DEN is further presented. Furthermore, the multiple life distributions are synthesized into the DT-DEN to tackle the epistemic uncertainty and mixed life distribution challenges. Specifically, the dynamic logic gates are converted into equivalent DENs with distinguished conditional mass tables, and then the belief interval of system reliability can be calculated by network forward reasoning. Finally, the availability and efficiency of the proposed method is verified by some numerical examples. Evidence theory Dynamic evidential networks Epistemic uncertainty Multiple life distribution Cheng, Yuhua (orcid)0000-0002-5488-4783 aut Song, Yufei aut Bai, Libing aut Chen, Kai aut Enthalten in Annals of operations research Springer US, 1984 311(2019), 1 vom: 10. Apr., Seite 311-333 (DE-627)12964370X (DE-600)252629-3 (DE-576)018141862 0254-5330 volume:311 year:2019 number:1 day:10 month:04 pages:311-333 https://doi.org/10.1007/s10479-019-03211-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-MAT AR 311 2019 1 10 04 311-333 |
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10.1007/s10479-019-03211-4 doi (DE-627)OLC2078229016 (DE-He213)s10479-019-03211-4-p DE-627 ger DE-627 rakwb eng 004 VZ 3,2 ssgn Mi, Jinhua verfasserin aut Application of dynamic evidential networks in reliability analysis of complex systems with epistemic uncertainty and multiple life distributions 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract With the modernization and intelligent of industrial equipment and systems, the challenges of dynamic characteristics, failure dependency and uncertainties have aroused by the increasing of system complexity. Besides, various types of components may follow different life distributions which bring the multiple life distributions problem in systems. In order to model the impact of time dependency and epistemic uncertainty on the failure behavior of system, this paper combines the flexible dynamic modeling with the uncertainty expression. Its advantages are intuitively graphical representation and reasoning that brought by evidential network (EN). After that, the discrete time dynamic evidential network (DT-DEN) is introduced to analyze the reliability of complex systems, and the network inference mechanism is clearly defined. The evidence theory and original definition and inference mechanism of conventional EN is firstly recommended, and the DT-DEN is further presented. Furthermore, the multiple life distributions are synthesized into the DT-DEN to tackle the epistemic uncertainty and mixed life distribution challenges. Specifically, the dynamic logic gates are converted into equivalent DENs with distinguished conditional mass tables, and then the belief interval of system reliability can be calculated by network forward reasoning. Finally, the availability and efficiency of the proposed method is verified by some numerical examples. Evidence theory Dynamic evidential networks Epistemic uncertainty Multiple life distribution Cheng, Yuhua (orcid)0000-0002-5488-4783 aut Song, Yufei aut Bai, Libing aut Chen, Kai aut Enthalten in Annals of operations research Springer US, 1984 311(2019), 1 vom: 10. Apr., Seite 311-333 (DE-627)12964370X (DE-600)252629-3 (DE-576)018141862 0254-5330 volume:311 year:2019 number:1 day:10 month:04 pages:311-333 https://doi.org/10.1007/s10479-019-03211-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-MAT AR 311 2019 1 10 04 311-333 |
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Application of dynamic evidential networks in reliability analysis of complex systems with epistemic uncertainty and multiple life distributions |
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Abstract With the modernization and intelligent of industrial equipment and systems, the challenges of dynamic characteristics, failure dependency and uncertainties have aroused by the increasing of system complexity. Besides, various types of components may follow different life distributions which bring the multiple life distributions problem in systems. In order to model the impact of time dependency and epistemic uncertainty on the failure behavior of system, this paper combines the flexible dynamic modeling with the uncertainty expression. Its advantages are intuitively graphical representation and reasoning that brought by evidential network (EN). After that, the discrete time dynamic evidential network (DT-DEN) is introduced to analyze the reliability of complex systems, and the network inference mechanism is clearly defined. The evidence theory and original definition and inference mechanism of conventional EN is firstly recommended, and the DT-DEN is further presented. Furthermore, the multiple life distributions are synthesized into the DT-DEN to tackle the epistemic uncertainty and mixed life distribution challenges. Specifically, the dynamic logic gates are converted into equivalent DENs with distinguished conditional mass tables, and then the belief interval of system reliability can be calculated by network forward reasoning. Finally, the availability and efficiency of the proposed method is verified by some numerical examples. © Springer Science+Business Media, LLC, part of Springer Nature 2019 |
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Abstract With the modernization and intelligent of industrial equipment and systems, the challenges of dynamic characteristics, failure dependency and uncertainties have aroused by the increasing of system complexity. Besides, various types of components may follow different life distributions which bring the multiple life distributions problem in systems. In order to model the impact of time dependency and epistemic uncertainty on the failure behavior of system, this paper combines the flexible dynamic modeling with the uncertainty expression. Its advantages are intuitively graphical representation and reasoning that brought by evidential network (EN). After that, the discrete time dynamic evidential network (DT-DEN) is introduced to analyze the reliability of complex systems, and the network inference mechanism is clearly defined. The evidence theory and original definition and inference mechanism of conventional EN is firstly recommended, and the DT-DEN is further presented. Furthermore, the multiple life distributions are synthesized into the DT-DEN to tackle the epistemic uncertainty and mixed life distribution challenges. Specifically, the dynamic logic gates are converted into equivalent DENs with distinguished conditional mass tables, and then the belief interval of system reliability can be calculated by network forward reasoning. Finally, the availability and efficiency of the proposed method is verified by some numerical examples. © Springer Science+Business Media, LLC, part of Springer Nature 2019 |
| abstract_unstemmed |
Abstract With the modernization and intelligent of industrial equipment and systems, the challenges of dynamic characteristics, failure dependency and uncertainties have aroused by the increasing of system complexity. Besides, various types of components may follow different life distributions which bring the multiple life distributions problem in systems. In order to model the impact of time dependency and epistemic uncertainty on the failure behavior of system, this paper combines the flexible dynamic modeling with the uncertainty expression. Its advantages are intuitively graphical representation and reasoning that brought by evidential network (EN). After that, the discrete time dynamic evidential network (DT-DEN) is introduced to analyze the reliability of complex systems, and the network inference mechanism is clearly defined. The evidence theory and original definition and inference mechanism of conventional EN is firstly recommended, and the DT-DEN is further presented. Furthermore, the multiple life distributions are synthesized into the DT-DEN to tackle the epistemic uncertainty and mixed life distribution challenges. Specifically, the dynamic logic gates are converted into equivalent DENs with distinguished conditional mass tables, and then the belief interval of system reliability can be calculated by network forward reasoning. Finally, the availability and efficiency of the proposed method is verified by some numerical examples. © Springer Science+Business Media, LLC, part of Springer Nature 2019 |
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Application of dynamic evidential networks in reliability analysis of complex systems with epistemic uncertainty and multiple life distributions |
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Cheng, Yuhua Song, Yufei Bai, Libing Chen, Kai |
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Cheng, Yuhua Song, Yufei Bai, Libing Chen, Kai |
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