An evidential network-based hierarchical method for system reliability analysis with common cause failures and mixed uncertainties

Redundant design has been widely used in aerospace systems, nuclear systems, etc. which calls for particular attention to common cause failure problems in such systems with various kinds of redundant mechanisms. Besides, imprecision and epistemic uncertainties also need to be taken into account for...
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Autor*in:	Mi, Jinhua [verfasserIn] Lu, Ning [verfasserIn] Li, Yan-Feng [verfasserIn] Huang, Hong-Zhong [verfasserIn] Bai, Libing [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Evidential network Hierarchical method Common cause failures Mixed uncertainties System reliability analysis

Übergeordnetes Werk:	Enthalten in: Reliability engineering & system safety - London [u.a.] : Elsevier Science, 1988, 220
Übergeordnetes Werk:	volume:220

DOI / URN:	10.1016/j.ress.2021.108295

Katalog-ID:	ELV007299044

Internformat


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245	1	0	\|a An evidential network-based hierarchical method for system reliability analysis with common cause failures and mixed uncertainties
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520			\|a Redundant design has been widely used in aerospace systems, nuclear systems, etc. which calls for particular attention to common cause failure problems in such systems with various kinds of redundant mechanisms. Besides, imprecision and epistemic uncertainties also need to be taken into account for system reliability modeling and assessment. In this paper, a comprehensive study based on the evidential network is performed for the reliability analysis of complex systems with common cause failures and mixed uncertainties. The decomposed partial α-factor is used to separate the contribution of independent parts and common cause parts of basic failure events. Mixed uncertainties are quantified and expressed by the D-S evidence theory, and the system reliability with uncertainties is modeled by evidential network. Furthermore, two layers, i.e. a decomposed event layer and coupling layer, are embedded into the evidential network of the system, and, as a result, the hierarchical structure of system reliability is constructed. The importance and sensitivities of various component types and their impact on system reliability are detected. The presented evidential network-based hierarchical method is applied to analyze the reliability of an auxiliary power supply system of a train and the results demonstrate the effectiveness of this method.
650		4	\|a Evidential network
650		4	\|a Hierarchical method
650		4	\|a Common cause failures
650		4	\|a Mixed uncertainties
650		4	\|a System reliability analysis
700	1		\|a Lu, Ning \|e verfasserin \|4 aut
700	1		\|a Li, Yan-Feng \|e verfasserin \|4 aut
700	1		\|a Huang, Hong-Zhong \|e verfasserin \|4 aut
700	1		\|a Bai, Libing \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Reliability engineering & system safety \|d London [u.a.] : Elsevier Science, 1988 \|g 220 \|h Online-Ressource \|w (DE-627)320608743 \|w (DE-600)2021091-7 \|w (DE-576)259485217 \|x 0951-8320 \|7 nnns
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912			\|a GBV_ILN_32
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912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_224
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2003
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912			\|a GBV_ILN_2005
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912			\|a GBV_ILN_2008
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912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2147
912			\|a GBV_ILN_2148
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
936	b	k	\|a 50.16 \|j Technische Zuverlässigkeit \|j Instandhaltung
936	b	k	\|a 85.38 \|j Qualitätsmanagement
951			\|a AR
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Indexfelder

author_variant	j m jm n l nl y f l yfl h z h hzh l b lb
matchkey_str	article:09518320:2021----::nvdnilewrbsdirrhclehdosserlaiiynlssihomnas
hierarchy_sort_str	2021
bklnumber	50.16 85.38
publishDate	2021
allfields	10.1016/j.ress.2021.108295 doi (DE-627)ELV007299044 (ELSEVIER)S0951-8320(21)00766-3 DE-627 ger DE-627 rda eng 600 DE-600 50.16 bkl 85.38 bkl Mi, Jinhua verfasserin aut An evidential network-based hierarchical method for system reliability analysis with common cause failures and mixed uncertainties 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Redundant design has been widely used in aerospace systems, nuclear systems, etc. which calls for particular attention to common cause failure problems in such systems with various kinds of redundant mechanisms. Besides, imprecision and epistemic uncertainties also need to be taken into account for system reliability modeling and assessment. In this paper, a comprehensive study based on the evidential network is performed for the reliability analysis of complex systems with common cause failures and mixed uncertainties. The decomposed partial α-factor is used to separate the contribution of independent parts and common cause parts of basic failure events. Mixed uncertainties are quantified and expressed by the D-S evidence theory, and the system reliability with uncertainties is modeled by evidential network. Furthermore, two layers, i.e. a decomposed event layer and coupling layer, are embedded into the evidential network of the system, and, as a result, the hierarchical structure of system reliability is constructed. The importance and sensitivities of various component types and their impact on system reliability are detected. The presented evidential network-based hierarchical method is applied to analyze the reliability of an auxiliary power supply system of a train and the results demonstrate the effectiveness of this method. Evidential network Hierarchical method Common cause failures Mixed uncertainties System reliability analysis Lu, Ning verfasserin aut Li, Yan-Feng verfasserin aut Huang, Hong-Zhong verfasserin aut Bai, Libing verfasserin aut Enthalten in Reliability engineering & system safety London [u.a.] : Elsevier Science, 1988 220 Online-Ressource (DE-627)320608743 (DE-600)2021091-7 (DE-576)259485217 0951-8320 nnns volume:220 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.16 Technische Zuverlässigkeit Instandhaltung 85.38 Qualitätsmanagement AR 220
spelling	10.1016/j.ress.2021.108295 doi (DE-627)ELV007299044 (ELSEVIER)S0951-8320(21)00766-3 DE-627 ger DE-627 rda eng 600 DE-600 50.16 bkl 85.38 bkl Mi, Jinhua verfasserin aut An evidential network-based hierarchical method for system reliability analysis with common cause failures and mixed uncertainties 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Redundant design has been widely used in aerospace systems, nuclear systems, etc. which calls for particular attention to common cause failure problems in such systems with various kinds of redundant mechanisms. Besides, imprecision and epistemic uncertainties also need to be taken into account for system reliability modeling and assessment. In this paper, a comprehensive study based on the evidential network is performed for the reliability analysis of complex systems with common cause failures and mixed uncertainties. The decomposed partial α-factor is used to separate the contribution of independent parts and common cause parts of basic failure events. Mixed uncertainties are quantified and expressed by the D-S evidence theory, and the system reliability with uncertainties is modeled by evidential network. Furthermore, two layers, i.e. a decomposed event layer and coupling layer, are embedded into the evidential network of the system, and, as a result, the hierarchical structure of system reliability is constructed. The importance and sensitivities of various component types and their impact on system reliability are detected. The presented evidential network-based hierarchical method is applied to analyze the reliability of an auxiliary power supply system of a train and the results demonstrate the effectiveness of this method. Evidential network Hierarchical method Common cause failures Mixed uncertainties System reliability analysis Lu, Ning verfasserin aut Li, Yan-Feng verfasserin aut Huang, Hong-Zhong verfasserin aut Bai, Libing verfasserin aut Enthalten in Reliability engineering & system safety London [u.a.] : Elsevier Science, 1988 220 Online-Ressource (DE-627)320608743 (DE-600)2021091-7 (DE-576)259485217 0951-8320 nnns volume:220 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.16 Technische Zuverlässigkeit Instandhaltung 85.38 Qualitätsmanagement AR 220
allfields_unstemmed	10.1016/j.ress.2021.108295 doi (DE-627)ELV007299044 (ELSEVIER)S0951-8320(21)00766-3 DE-627 ger DE-627 rda eng 600 DE-600 50.16 bkl 85.38 bkl Mi, Jinhua verfasserin aut An evidential network-based hierarchical method for system reliability analysis with common cause failures and mixed uncertainties 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Redundant design has been widely used in aerospace systems, nuclear systems, etc. which calls for particular attention to common cause failure problems in such systems with various kinds of redundant mechanisms. Besides, imprecision and epistemic uncertainties also need to be taken into account for system reliability modeling and assessment. In this paper, a comprehensive study based on the evidential network is performed for the reliability analysis of complex systems with common cause failures and mixed uncertainties. The decomposed partial α-factor is used to separate the contribution of independent parts and common cause parts of basic failure events. Mixed uncertainties are quantified and expressed by the D-S evidence theory, and the system reliability with uncertainties is modeled by evidential network. Furthermore, two layers, i.e. a decomposed event layer and coupling layer, are embedded into the evidential network of the system, and, as a result, the hierarchical structure of system reliability is constructed. The importance and sensitivities of various component types and their impact on system reliability are detected. The presented evidential network-based hierarchical method is applied to analyze the reliability of an auxiliary power supply system of a train and the results demonstrate the effectiveness of this method. Evidential network Hierarchical method Common cause failures Mixed uncertainties System reliability analysis Lu, Ning verfasserin aut Li, Yan-Feng verfasserin aut Huang, Hong-Zhong verfasserin aut Bai, Libing verfasserin aut Enthalten in Reliability engineering & system safety London [u.a.] : Elsevier Science, 1988 220 Online-Ressource (DE-627)320608743 (DE-600)2021091-7 (DE-576)259485217 0951-8320 nnns volume:220 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.16 Technische Zuverlässigkeit Instandhaltung 85.38 Qualitätsmanagement AR 220
allfieldsGer	10.1016/j.ress.2021.108295 doi (DE-627)ELV007299044 (ELSEVIER)S0951-8320(21)00766-3 DE-627 ger DE-627 rda eng 600 DE-600 50.16 bkl 85.38 bkl Mi, Jinhua verfasserin aut An evidential network-based hierarchical method for system reliability analysis with common cause failures and mixed uncertainties 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Redundant design has been widely used in aerospace systems, nuclear systems, etc. which calls for particular attention to common cause failure problems in such systems with various kinds of redundant mechanisms. Besides, imprecision and epistemic uncertainties also need to be taken into account for system reliability modeling and assessment. In this paper, a comprehensive study based on the evidential network is performed for the reliability analysis of complex systems with common cause failures and mixed uncertainties. The decomposed partial α-factor is used to separate the contribution of independent parts and common cause parts of basic failure events. Mixed uncertainties are quantified and expressed by the D-S evidence theory, and the system reliability with uncertainties is modeled by evidential network. Furthermore, two layers, i.e. a decomposed event layer and coupling layer, are embedded into the evidential network of the system, and, as a result, the hierarchical structure of system reliability is constructed. The importance and sensitivities of various component types and their impact on system reliability are detected. The presented evidential network-based hierarchical method is applied to analyze the reliability of an auxiliary power supply system of a train and the results demonstrate the effectiveness of this method. Evidential network Hierarchical method Common cause failures Mixed uncertainties System reliability analysis Lu, Ning verfasserin aut Li, Yan-Feng verfasserin aut Huang, Hong-Zhong verfasserin aut Bai, Libing verfasserin aut Enthalten in Reliability engineering & system safety London [u.a.] : Elsevier Science, 1988 220 Online-Ressource (DE-627)320608743 (DE-600)2021091-7 (DE-576)259485217 0951-8320 nnns volume:220 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.16 Technische Zuverlässigkeit Instandhaltung 85.38 Qualitätsmanagement AR 220
allfieldsSound	10.1016/j.ress.2021.108295 doi (DE-627)ELV007299044 (ELSEVIER)S0951-8320(21)00766-3 DE-627 ger DE-627 rda eng 600 DE-600 50.16 bkl 85.38 bkl Mi, Jinhua verfasserin aut An evidential network-based hierarchical method for system reliability analysis with common cause failures and mixed uncertainties 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Redundant design has been widely used in aerospace systems, nuclear systems, etc. which calls for particular attention to common cause failure problems in such systems with various kinds of redundant mechanisms. Besides, imprecision and epistemic uncertainties also need to be taken into account for system reliability modeling and assessment. In this paper, a comprehensive study based on the evidential network is performed for the reliability analysis of complex systems with common cause failures and mixed uncertainties. The decomposed partial α-factor is used to separate the contribution of independent parts and common cause parts of basic failure events. Mixed uncertainties are quantified and expressed by the D-S evidence theory, and the system reliability with uncertainties is modeled by evidential network. Furthermore, two layers, i.e. a decomposed event layer and coupling layer, are embedded into the evidential network of the system, and, as a result, the hierarchical structure of system reliability is constructed. The importance and sensitivities of various component types and their impact on system reliability are detected. The presented evidential network-based hierarchical method is applied to analyze the reliability of an auxiliary power supply system of a train and the results demonstrate the effectiveness of this method. Evidential network Hierarchical method Common cause failures Mixed uncertainties System reliability analysis Lu, Ning verfasserin aut Li, Yan-Feng verfasserin aut Huang, Hong-Zhong verfasserin aut Bai, Libing verfasserin aut Enthalten in Reliability engineering & system safety London [u.a.] : Elsevier Science, 1988 220 Online-Ressource (DE-627)320608743 (DE-600)2021091-7 (DE-576)259485217 0951-8320 nnns volume:220 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.16 Technische Zuverlässigkeit Instandhaltung 85.38 Qualitätsmanagement AR 220
language	English
source	Enthalten in Reliability engineering & system safety 220 volume:220
sourceStr	Enthalten in Reliability engineering & system safety 220 volume:220
format_phy_str_mv	Article
bklname	Technische Zuverlässigkeit Instandhaltung Qualitätsmanagement
institution	findex.gbv.de
topic_facet	Evidential network Hierarchical method Common cause failures Mixed uncertainties System reliability analysis
dewey-raw	600
isfreeaccess_bool	false
container_title	Reliability engineering & system safety
authorswithroles_txt_mv	Mi, Jinhua @@aut@@ Lu, Ning @@aut@@ Li, Yan-Feng @@aut@@ Huang, Hong-Zhong @@aut@@ Bai, Libing @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	320608743
dewey-sort	3600
id	ELV007299044
language_de	englisch
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author	Mi, Jinhua
spellingShingle	Mi, Jinhua ddc 600 bkl 50.16 bkl 85.38 misc Evidential network misc Hierarchical method misc Common cause failures misc Mixed uncertainties misc System reliability analysis An evidential network-based hierarchical method for system reliability analysis with common cause failures and mixed uncertainties
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topic_title	600 DE-600 50.16 bkl 85.38 bkl An evidential network-based hierarchical method for system reliability analysis with common cause failures and mixed uncertainties Evidential network Hierarchical method Common cause failures Mixed uncertainties System reliability analysis
topic	ddc 600 bkl 50.16 bkl 85.38 misc Evidential network misc Hierarchical method misc Common cause failures misc Mixed uncertainties misc System reliability analysis
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title	An evidential network-based hierarchical method for system reliability analysis with common cause failures and mixed uncertainties
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title_full	An evidential network-based hierarchical method for system reliability analysis with common cause failures and mixed uncertainties
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title_sort	an evidential network-based hierarchical method for system reliability analysis with common cause failures and mixed uncertainties
title_auth	An evidential network-based hierarchical method for system reliability analysis with common cause failures and mixed uncertainties
abstract	Redundant design has been widely used in aerospace systems, nuclear systems, etc. which calls for particular attention to common cause failure problems in such systems with various kinds of redundant mechanisms. Besides, imprecision and epistemic uncertainties also need to be taken into account for system reliability modeling and assessment. In this paper, a comprehensive study based on the evidential network is performed for the reliability analysis of complex systems with common cause failures and mixed uncertainties. The decomposed partial α-factor is used to separate the contribution of independent parts and common cause parts of basic failure events. Mixed uncertainties are quantified and expressed by the D-S evidence theory, and the system reliability with uncertainties is modeled by evidential network. Furthermore, two layers, i.e. a decomposed event layer and coupling layer, are embedded into the evidential network of the system, and, as a result, the hierarchical structure of system reliability is constructed. The importance and sensitivities of various component types and their impact on system reliability are detected. The presented evidential network-based hierarchical method is applied to analyze the reliability of an auxiliary power supply system of a train and the results demonstrate the effectiveness of this method.
abstractGer	Redundant design has been widely used in aerospace systems, nuclear systems, etc. which calls for particular attention to common cause failure problems in such systems with various kinds of redundant mechanisms. Besides, imprecision and epistemic uncertainties also need to be taken into account for system reliability modeling and assessment. In this paper, a comprehensive study based on the evidential network is performed for the reliability analysis of complex systems with common cause failures and mixed uncertainties. The decomposed partial α-factor is used to separate the contribution of independent parts and common cause parts of basic failure events. Mixed uncertainties are quantified and expressed by the D-S evidence theory, and the system reliability with uncertainties is modeled by evidential network. Furthermore, two layers, i.e. a decomposed event layer and coupling layer, are embedded into the evidential network of the system, and, as a result, the hierarchical structure of system reliability is constructed. The importance and sensitivities of various component types and their impact on system reliability are detected. The presented evidential network-based hierarchical method is applied to analyze the reliability of an auxiliary power supply system of a train and the results demonstrate the effectiveness of this method.
abstract_unstemmed	Redundant design has been widely used in aerospace systems, nuclear systems, etc. which calls for particular attention to common cause failure problems in such systems with various kinds of redundant mechanisms. Besides, imprecision and epistemic uncertainties also need to be taken into account for system reliability modeling and assessment. In this paper, a comprehensive study based on the evidential network is performed for the reliability analysis of complex systems with common cause failures and mixed uncertainties. The decomposed partial α-factor is used to separate the contribution of independent parts and common cause parts of basic failure events. Mixed uncertainties are quantified and expressed by the D-S evidence theory, and the system reliability with uncertainties is modeled by evidential network. Furthermore, two layers, i.e. a decomposed event layer and coupling layer, are embedded into the evidential network of the system, and, as a result, the hierarchical structure of system reliability is constructed. The importance and sensitivities of various component types and their impact on system reliability are detected. The presented evidential network-based hierarchical method is applied to analyze the reliability of an auxiliary power supply system of a train and the results demonstrate the effectiveness of this method.
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title_short	An evidential network-based hierarchical method for system reliability analysis with common cause failures and mixed uncertainties
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author2	Lu, Ning Li, Yan-Feng Huang, Hong-Zhong Bai, Libing
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An evidential network-based hierarchical method for system reliability analysis with common cause failures and mixed uncertainties

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