Adaptive vectorial surrogate modeling framework for multi-objective reliability estimation

Vectorial modeling concept is proposed in this paper by introducing the matrix theory into the point modeling concept (surrogate modeling strategy), and an adaptive vectorial surrogate modeling framework (AVSMF, short for) is developed based on the vectorial modeling concept and adaptive modeling st...
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Gespeichert in:

Autor*in:	Lu, Cheng [verfasserIn] Teng, Da [verfasserIn] Chen, Jun-Yu [verfasserIn] Fei, Cheng-Wei [verfasserIn] Keshtegar, Behrooz [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Reliability estimation Multi-objective structure Adaptive vectorial surrogate modeling framework Point surrogate modeling concept Vectorial modeling concept

Übergeordnetes Werk:	Enthalten in: Reliability engineering & system safety - London [u.a.] : Elsevier Science, 1988, 234
Übergeordnetes Werk:	volume:234

DOI / URN:	10.1016/j.ress.2023.109148

Katalog-ID:	ELV063763397

Internformat


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245	1	0	\|a Adaptive vectorial surrogate modeling framework for multi-objective reliability estimation
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520			\|a Vectorial modeling concept is proposed in this paper by introducing the matrix theory into the point modeling concept (surrogate modeling strategy), and an adaptive vectorial surrogate modeling framework (AVSMF, short for) is developed based on the vectorial modeling concept and adaptive modeling strategy. Herein, the adaptive modeling strategy is adopted to determine the form of mathematical model of each objective in line with the cost function, the surrogate modeling strategy is regarded as the basis function for reflecting the relationship of the output of single-objective between the relevant inputs, and the matrix theory is used to ascertain the vectors and cell arrays of undetermined parameters and to establish the performance function of multi-objective structures. To validate the proposed method, we use three examples including approximate and probabilistic analysis of nonlinear function with multiple responses, reliability evaluation of landing gear brake system temperature and reliability assessment of aeroengine high-pressure turbine blisk stress, strain and deformation, to demonstrate the effectiveness of the developed AVSMF. Besides, the modeling and simulation properties are verified by comparison of different methods. The results show that the proposed AVSMF has obvious advantages in the computational efficiency and precision.
650		4	\|a Reliability estimation
650		4	\|a Multi-objective structure
650		4	\|a Adaptive vectorial surrogate modeling framework
650		4	\|a Point surrogate modeling concept
650		4	\|a Vectorial modeling concept
700	1		\|a Teng, Da \|e verfasserin \|4 aut
700	1		\|a Chen, Jun-Yu \|e verfasserin \|4 aut
700	1		\|a Fei, Cheng-Wei \|e verfasserin \|0 (orcid)0000-0001-5333-1055 \|4 aut
700	1		\|a Keshtegar, Behrooz \|e verfasserin \|4 aut
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author_variant	c l cl d t dt j y c jyc c w f cwf b k bk
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publishDate	2023
allfields	10.1016/j.ress.2023.109148 doi (DE-627)ELV063763397 (ELSEVIER)S0951-8320(23)00063-7 DE-627 ger DE-627 rda eng 600 VZ 50.16 bkl 85.38 bkl Lu, Cheng verfasserin (orcid)0000-0002-5939-1048 aut Adaptive vectorial surrogate modeling framework for multi-objective reliability estimation 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Vectorial modeling concept is proposed in this paper by introducing the matrix theory into the point modeling concept (surrogate modeling strategy), and an adaptive vectorial surrogate modeling framework (AVSMF, short for) is developed based on the vectorial modeling concept and adaptive modeling strategy. Herein, the adaptive modeling strategy is adopted to determine the form of mathematical model of each objective in line with the cost function, the surrogate modeling strategy is regarded as the basis function for reflecting the relationship of the output of single-objective between the relevant inputs, and the matrix theory is used to ascertain the vectors and cell arrays of undetermined parameters and to establish the performance function of multi-objective structures. To validate the proposed method, we use three examples including approximate and probabilistic analysis of nonlinear function with multiple responses, reliability evaluation of landing gear brake system temperature and reliability assessment of aeroengine high-pressure turbine blisk stress, strain and deformation, to demonstrate the effectiveness of the developed AVSMF. Besides, the modeling and simulation properties are verified by comparison of different methods. The results show that the proposed AVSMF has obvious advantages in the computational efficiency and precision. Reliability estimation Multi-objective structure Adaptive vectorial surrogate modeling framework Point surrogate modeling concept Vectorial modeling concept Teng, Da verfasserin aut Chen, Jun-Yu verfasserin aut Fei, Cheng-Wei verfasserin (orcid)0000-0001-5333-1055 aut Keshtegar, Behrooz verfasserin aut Enthalten in Reliability engineering & system safety London [u.a.] : Elsevier Science, 1988 234 Online-Ressource (DE-627)320608743 (DE-600)2021091-7 (DE-576)259485217 0951-8320 nnns volume:234 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 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_4338 GBV_ILN_4393 GBV_ILN_4700 50.16 Technische Zuverlässigkeit Instandhaltung VZ 85.38 Qualitätsmanagement VZ AR 234
spelling	10.1016/j.ress.2023.109148 doi (DE-627)ELV063763397 (ELSEVIER)S0951-8320(23)00063-7 DE-627 ger DE-627 rda eng 600 VZ 50.16 bkl 85.38 bkl Lu, Cheng verfasserin (orcid)0000-0002-5939-1048 aut Adaptive vectorial surrogate modeling framework for multi-objective reliability estimation 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Vectorial modeling concept is proposed in this paper by introducing the matrix theory into the point modeling concept (surrogate modeling strategy), and an adaptive vectorial surrogate modeling framework (AVSMF, short for) is developed based on the vectorial modeling concept and adaptive modeling strategy. Herein, the adaptive modeling strategy is adopted to determine the form of mathematical model of each objective in line with the cost function, the surrogate modeling strategy is regarded as the basis function for reflecting the relationship of the output of single-objective between the relevant inputs, and the matrix theory is used to ascertain the vectors and cell arrays of undetermined parameters and to establish the performance function of multi-objective structures. To validate the proposed method, we use three examples including approximate and probabilistic analysis of nonlinear function with multiple responses, reliability evaluation of landing gear brake system temperature and reliability assessment of aeroengine high-pressure turbine blisk stress, strain and deformation, to demonstrate the effectiveness of the developed AVSMF. Besides, the modeling and simulation properties are verified by comparison of different methods. The results show that the proposed AVSMF has obvious advantages in the computational efficiency and precision. Reliability estimation Multi-objective structure Adaptive vectorial surrogate modeling framework Point surrogate modeling concept Vectorial modeling concept Teng, Da verfasserin aut Chen, Jun-Yu verfasserin aut Fei, Cheng-Wei verfasserin (orcid)0000-0001-5333-1055 aut Keshtegar, Behrooz verfasserin aut Enthalten in Reliability engineering & system safety London [u.a.] : Elsevier Science, 1988 234 Online-Ressource (DE-627)320608743 (DE-600)2021091-7 (DE-576)259485217 0951-8320 nnns volume:234 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 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_4338 GBV_ILN_4393 GBV_ILN_4700 50.16 Technische Zuverlässigkeit Instandhaltung VZ 85.38 Qualitätsmanagement VZ AR 234
allfields_unstemmed	10.1016/j.ress.2023.109148 doi (DE-627)ELV063763397 (ELSEVIER)S0951-8320(23)00063-7 DE-627 ger DE-627 rda eng 600 VZ 50.16 bkl 85.38 bkl Lu, Cheng verfasserin (orcid)0000-0002-5939-1048 aut Adaptive vectorial surrogate modeling framework for multi-objective reliability estimation 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Vectorial modeling concept is proposed in this paper by introducing the matrix theory into the point modeling concept (surrogate modeling strategy), and an adaptive vectorial surrogate modeling framework (AVSMF, short for) is developed based on the vectorial modeling concept and adaptive modeling strategy. Herein, the adaptive modeling strategy is adopted to determine the form of mathematical model of each objective in line with the cost function, the surrogate modeling strategy is regarded as the basis function for reflecting the relationship of the output of single-objective between the relevant inputs, and the matrix theory is used to ascertain the vectors and cell arrays of undetermined parameters and to establish the performance function of multi-objective structures. To validate the proposed method, we use three examples including approximate and probabilistic analysis of nonlinear function with multiple responses, reliability evaluation of landing gear brake system temperature and reliability assessment of aeroengine high-pressure turbine blisk stress, strain and deformation, to demonstrate the effectiveness of the developed AVSMF. Besides, the modeling and simulation properties are verified by comparison of different methods. The results show that the proposed AVSMF has obvious advantages in the computational efficiency and precision. Reliability estimation Multi-objective structure Adaptive vectorial surrogate modeling framework Point surrogate modeling concept Vectorial modeling concept Teng, Da verfasserin aut Chen, Jun-Yu verfasserin aut Fei, Cheng-Wei verfasserin (orcid)0000-0001-5333-1055 aut Keshtegar, Behrooz verfasserin aut Enthalten in Reliability engineering & system safety London [u.a.] : Elsevier Science, 1988 234 Online-Ressource (DE-627)320608743 (DE-600)2021091-7 (DE-576)259485217 0951-8320 nnns volume:234 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 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_4338 GBV_ILN_4393 GBV_ILN_4700 50.16 Technische Zuverlässigkeit Instandhaltung VZ 85.38 Qualitätsmanagement VZ AR 234
allfieldsGer	10.1016/j.ress.2023.109148 doi (DE-627)ELV063763397 (ELSEVIER)S0951-8320(23)00063-7 DE-627 ger DE-627 rda eng 600 VZ 50.16 bkl 85.38 bkl Lu, Cheng verfasserin (orcid)0000-0002-5939-1048 aut Adaptive vectorial surrogate modeling framework for multi-objective reliability estimation 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Vectorial modeling concept is proposed in this paper by introducing the matrix theory into the point modeling concept (surrogate modeling strategy), and an adaptive vectorial surrogate modeling framework (AVSMF, short for) is developed based on the vectorial modeling concept and adaptive modeling strategy. Herein, the adaptive modeling strategy is adopted to determine the form of mathematical model of each objective in line with the cost function, the surrogate modeling strategy is regarded as the basis function for reflecting the relationship of the output of single-objective between the relevant inputs, and the matrix theory is used to ascertain the vectors and cell arrays of undetermined parameters and to establish the performance function of multi-objective structures. To validate the proposed method, we use three examples including approximate and probabilistic analysis of nonlinear function with multiple responses, reliability evaluation of landing gear brake system temperature and reliability assessment of aeroengine high-pressure turbine blisk stress, strain and deformation, to demonstrate the effectiveness of the developed AVSMF. Besides, the modeling and simulation properties are verified by comparison of different methods. The results show that the proposed AVSMF has obvious advantages in the computational efficiency and precision. Reliability estimation Multi-objective structure Adaptive vectorial surrogate modeling framework Point surrogate modeling concept Vectorial modeling concept Teng, Da verfasserin aut Chen, Jun-Yu verfasserin aut Fei, Cheng-Wei verfasserin (orcid)0000-0001-5333-1055 aut Keshtegar, Behrooz verfasserin aut Enthalten in Reliability engineering & system safety London [u.a.] : Elsevier Science, 1988 234 Online-Ressource (DE-627)320608743 (DE-600)2021091-7 (DE-576)259485217 0951-8320 nnns volume:234 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 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_4338 GBV_ILN_4393 GBV_ILN_4700 50.16 Technische Zuverlässigkeit Instandhaltung VZ 85.38 Qualitätsmanagement VZ AR 234
allfieldsSound	10.1016/j.ress.2023.109148 doi (DE-627)ELV063763397 (ELSEVIER)S0951-8320(23)00063-7 DE-627 ger DE-627 rda eng 600 VZ 50.16 bkl 85.38 bkl Lu, Cheng verfasserin (orcid)0000-0002-5939-1048 aut Adaptive vectorial surrogate modeling framework for multi-objective reliability estimation 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Vectorial modeling concept is proposed in this paper by introducing the matrix theory into the point modeling concept (surrogate modeling strategy), and an adaptive vectorial surrogate modeling framework (AVSMF, short for) is developed based on the vectorial modeling concept and adaptive modeling strategy. Herein, the adaptive modeling strategy is adopted to determine the form of mathematical model of each objective in line with the cost function, the surrogate modeling strategy is regarded as the basis function for reflecting the relationship of the output of single-objective between the relevant inputs, and the matrix theory is used to ascertain the vectors and cell arrays of undetermined parameters and to establish the performance function of multi-objective structures. To validate the proposed method, we use three examples including approximate and probabilistic analysis of nonlinear function with multiple responses, reliability evaluation of landing gear brake system temperature and reliability assessment of aeroengine high-pressure turbine blisk stress, strain and deformation, to demonstrate the effectiveness of the developed AVSMF. Besides, the modeling and simulation properties are verified by comparison of different methods. The results show that the proposed AVSMF has obvious advantages in the computational efficiency and precision. Reliability estimation Multi-objective structure Adaptive vectorial surrogate modeling framework Point surrogate modeling concept Vectorial modeling concept Teng, Da verfasserin aut Chen, Jun-Yu verfasserin aut Fei, Cheng-Wei verfasserin (orcid)0000-0001-5333-1055 aut Keshtegar, Behrooz verfasserin aut Enthalten in Reliability engineering & system safety London [u.a.] : Elsevier Science, 1988 234 Online-Ressource (DE-627)320608743 (DE-600)2021091-7 (DE-576)259485217 0951-8320 nnns volume:234 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 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_4338 GBV_ILN_4393 GBV_ILN_4700 50.16 Technische Zuverlässigkeit Instandhaltung VZ 85.38 Qualitätsmanagement VZ AR 234
language	English
source	Enthalten in Reliability engineering & system safety 234 volume:234
sourceStr	Enthalten in Reliability engineering & system safety 234 volume:234
format_phy_str_mv	Article
bklname	Technische Zuverlässigkeit Instandhaltung Qualitätsmanagement
institution	findex.gbv.de
topic_facet	Reliability estimation Multi-objective structure Adaptive vectorial surrogate modeling framework Point surrogate modeling concept Vectorial modeling concept
dewey-raw	600
isfreeaccess_bool	false
container_title	Reliability engineering & system safety
authorswithroles_txt_mv	Lu, Cheng @@aut@@ Teng, Da @@aut@@ Chen, Jun-Yu @@aut@@ Fei, Cheng-Wei @@aut@@ Keshtegar, Behrooz @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	320608743
dewey-sort	3600
id	ELV063763397
language_de	englisch
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author	Lu, Cheng
spellingShingle	Lu, Cheng ddc 600 bkl 50.16 bkl 85.38 misc Reliability estimation misc Multi-objective structure misc Adaptive vectorial surrogate modeling framework misc Point surrogate modeling concept misc Vectorial modeling concept Adaptive vectorial surrogate modeling framework for multi-objective reliability estimation
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topic_title	600 VZ 50.16 bkl 85.38 bkl Adaptive vectorial surrogate modeling framework for multi-objective reliability estimation Reliability estimation Multi-objective structure Adaptive vectorial surrogate modeling framework Point surrogate modeling concept Vectorial modeling concept
topic	ddc 600 bkl 50.16 bkl 85.38 misc Reliability estimation misc Multi-objective structure misc Adaptive vectorial surrogate modeling framework misc Point surrogate modeling concept misc Vectorial modeling concept
topic_unstemmed	ddc 600 bkl 50.16 bkl 85.38 misc Reliability estimation misc Multi-objective structure misc Adaptive vectorial surrogate modeling framework misc Point surrogate modeling concept misc Vectorial modeling concept
topic_browse	ddc 600 bkl 50.16 bkl 85.38 misc Reliability estimation misc Multi-objective structure misc Adaptive vectorial surrogate modeling framework misc Point surrogate modeling concept misc Vectorial modeling concept
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title	Adaptive vectorial surrogate modeling framework for multi-objective reliability estimation
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title_full	Adaptive vectorial surrogate modeling framework for multi-objective reliability estimation
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title_sort	adaptive vectorial surrogate modeling framework for multi-objective reliability estimation
title_auth	Adaptive vectorial surrogate modeling framework for multi-objective reliability estimation
abstract	Vectorial modeling concept is proposed in this paper by introducing the matrix theory into the point modeling concept (surrogate modeling strategy), and an adaptive vectorial surrogate modeling framework (AVSMF, short for) is developed based on the vectorial modeling concept and adaptive modeling strategy. Herein, the adaptive modeling strategy is adopted to determine the form of mathematical model of each objective in line with the cost function, the surrogate modeling strategy is regarded as the basis function for reflecting the relationship of the output of single-objective between the relevant inputs, and the matrix theory is used to ascertain the vectors and cell arrays of undetermined parameters and to establish the performance function of multi-objective structures. To validate the proposed method, we use three examples including approximate and probabilistic analysis of nonlinear function with multiple responses, reliability evaluation of landing gear brake system temperature and reliability assessment of aeroengine high-pressure turbine blisk stress, strain and deformation, to demonstrate the effectiveness of the developed AVSMF. Besides, the modeling and simulation properties are verified by comparison of different methods. The results show that the proposed AVSMF has obvious advantages in the computational efficiency and precision.
abstractGer	Vectorial modeling concept is proposed in this paper by introducing the matrix theory into the point modeling concept (surrogate modeling strategy), and an adaptive vectorial surrogate modeling framework (AVSMF, short for) is developed based on the vectorial modeling concept and adaptive modeling strategy. Herein, the adaptive modeling strategy is adopted to determine the form of mathematical model of each objective in line with the cost function, the surrogate modeling strategy is regarded as the basis function for reflecting the relationship of the output of single-objective between the relevant inputs, and the matrix theory is used to ascertain the vectors and cell arrays of undetermined parameters and to establish the performance function of multi-objective structures. To validate the proposed method, we use three examples including approximate and probabilistic analysis of nonlinear function with multiple responses, reliability evaluation of landing gear brake system temperature and reliability assessment of aeroengine high-pressure turbine blisk stress, strain and deformation, to demonstrate the effectiveness of the developed AVSMF. Besides, the modeling and simulation properties are verified by comparison of different methods. The results show that the proposed AVSMF has obvious advantages in the computational efficiency and precision.
abstract_unstemmed	Vectorial modeling concept is proposed in this paper by introducing the matrix theory into the point modeling concept (surrogate modeling strategy), and an adaptive vectorial surrogate modeling framework (AVSMF, short for) is developed based on the vectorial modeling concept and adaptive modeling strategy. Herein, the adaptive modeling strategy is adopted to determine the form of mathematical model of each objective in line with the cost function, the surrogate modeling strategy is regarded as the basis function for reflecting the relationship of the output of single-objective between the relevant inputs, and the matrix theory is used to ascertain the vectors and cell arrays of undetermined parameters and to establish the performance function of multi-objective structures. To validate the proposed method, we use three examples including approximate and probabilistic analysis of nonlinear function with multiple responses, reliability evaluation of landing gear brake system temperature and reliability assessment of aeroengine high-pressure turbine blisk stress, strain and deformation, to demonstrate the effectiveness of the developed AVSMF. Besides, the modeling and simulation properties are verified by comparison of different methods. The results show that the proposed AVSMF has obvious advantages in the computational efficiency and precision.
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title_short	Adaptive vectorial surrogate modeling framework for multi-objective reliability estimation
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author2	Teng, Da Chen, Jun-Yu Fei, Cheng-Wei Keshtegar, Behrooz
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up_date	2024-07-06T19:57:20.775Z
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Adaptive vectorial surrogate modeling framework for multi-objective reliability estimation

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