On choice and effect of weight matrix for response sensitivity-based damage identification with measurement and model errors

This paper aims to present a thorough view on the choice and effect of the weight matrix for response sensitivity-based damage identification with measurement and/or model errors. The derivation of the optimal weight matrix is mainly twofold. On the one hand, when only measurement errors are involve...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Lu, Zhong-Rong [verfasserIn] Zhou, Junxian [verfasserIn] Wang, Li [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Optimal weight matrix Structural damage identification Measurement and model errors Enhanced response sensitivity approach Hybrid data

Übergeordnetes Werk:	Enthalten in: Mechanical systems and signal processing - Amsterdam [u.a.] : Elsevier, 1987, 114, Seite 1-24
Übergeordnetes Werk:	volume:114 ; pages:1-24

DOI / URN:	10.1016/j.ymssp.2018.05.007

Katalog-ID:	ELV002521466

Internformat


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245	1	0	\|a On choice and effect of weight matrix for response sensitivity-based damage identification with measurement and model errors
264		1	\|c 2018
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338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a This paper aims to present a thorough view on the choice and effect of the weight matrix for response sensitivity-based damage identification with measurement and/or model errors. The derivation of the optimal weight matrix is mainly twofold. On the one hand, when only measurement errors are involved, the optimal weight matrix is found to be inverse proportional to the measurement error covariance by minimizing the expectation of squares error of the whole identification results. On the other hand, if model errors are additionally considered, the optimal weight matrix then depends not only on the measurement error covariance, but also on the model error covariance. Further analysis reveals that the optimal weight matrix can also make the ‘relative error’—square-root of expectation of squares error in every individual damage parameter minimized. Then, the effect of the proposed optimal weight matrix with measurement and/or model errors is studied on two typical examples—a plane frame and a simply-supported plate. Results show that when hybrid types of measurement data—accelerations, displacements and/or eigenfrequencies are used or when the response data is sensitive to model errors, the optimal weight matrix should be invoked to get reasonably good identification results and the improvements brought by the optimal weight matrix are substantial. The whole work shall be instructive for damage identification when different types of measurements are available and when model errors are non-negligible.
650		4	\|a Optimal weight matrix
650		4	\|a Structural damage identification
650		4	\|a Measurement and model errors
650		4	\|a Enhanced response sensitivity approach
650		4	\|a Hybrid data
700	1		\|a Zhou, Junxian \|e verfasserin \|4 aut
700	1		\|a Wang, Li \|e verfasserin \|0 (orcid)0000-0002-8556-5670 \|4 aut
773	0	8	\|i Enthalten in \|t Mechanical systems and signal processing \|d Amsterdam [u.a.] : Elsevier, 1987 \|g 114, Seite 1-24 \|h Online-Ressource \|w (DE-627)267838670 \|w (DE-600)1471003-1 \|w (DE-576)253127629 \|x 1096-1216 \|7 nnns
773	1	8	\|g volume:114 \|g pages:1-24
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912			\|a GBV_ILN_32
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912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
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_101
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
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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
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912			\|a GBV_ILN_2152
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912			\|a GBV_ILN_2470
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912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
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912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
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 50.32 \|j Dynamik \|j Schwingungslehre \|x Technische Mechanik
936	b	k	\|a 50.16 \|j Technische Zuverlässigkeit \|j Instandhaltung
951			\|a AR
952			\|d 114 \|h 1-24

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publishDate	2018
allfields	10.1016/j.ymssp.2018.05.007 doi (DE-627)ELV002521466 (ELSEVIER)S0888-3270(18)30264-4 DE-627 ger DE-627 rda eng 004 DE-600 50.32 bkl 50.16 bkl Lu, Zhong-Rong verfasserin aut On choice and effect of weight matrix for response sensitivity-based damage identification with measurement and model errors 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper aims to present a thorough view on the choice and effect of the weight matrix for response sensitivity-based damage identification with measurement and/or model errors. The derivation of the optimal weight matrix is mainly twofold. On the one hand, when only measurement errors are involved, the optimal weight matrix is found to be inverse proportional to the measurement error covariance by minimizing the expectation of squares error of the whole identification results. On the other hand, if model errors are additionally considered, the optimal weight matrix then depends not only on the measurement error covariance, but also on the model error covariance. Further analysis reveals that the optimal weight matrix can also make the ‘relative error’—square-root of expectation of squares error in every individual damage parameter minimized. Then, the effect of the proposed optimal weight matrix with measurement and/or model errors is studied on two typical examples—a plane frame and a simply-supported plate. Results show that when hybrid types of measurement data—accelerations, displacements and/or eigenfrequencies are used or when the response data is sensitive to model errors, the optimal weight matrix should be invoked to get reasonably good identification results and the improvements brought by the optimal weight matrix are substantial. The whole work shall be instructive for damage identification when different types of measurements are available and when model errors are non-negligible. Optimal weight matrix Structural damage identification Measurement and model errors Enhanced response sensitivity approach Hybrid data Zhou, Junxian verfasserin aut Wang, Li verfasserin (orcid)0000-0002-8556-5670 aut Enthalten in Mechanical systems and signal processing Amsterdam [u.a.] : Elsevier, 1987 114, Seite 1-24 Online-Ressource (DE-627)267838670 (DE-600)1471003-1 (DE-576)253127629 1096-1216 nnns volume:114 pages:1-24 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_101 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_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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.32 Dynamik Schwingungslehre Technische Mechanik 50.16 Technische Zuverlässigkeit Instandhaltung AR 114 1-24
spelling	10.1016/j.ymssp.2018.05.007 doi (DE-627)ELV002521466 (ELSEVIER)S0888-3270(18)30264-4 DE-627 ger DE-627 rda eng 004 DE-600 50.32 bkl 50.16 bkl Lu, Zhong-Rong verfasserin aut On choice and effect of weight matrix for response sensitivity-based damage identification with measurement and model errors 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper aims to present a thorough view on the choice and effect of the weight matrix for response sensitivity-based damage identification with measurement and/or model errors. The derivation of the optimal weight matrix is mainly twofold. On the one hand, when only measurement errors are involved, the optimal weight matrix is found to be inverse proportional to the measurement error covariance by minimizing the expectation of squares error of the whole identification results. On the other hand, if model errors are additionally considered, the optimal weight matrix then depends not only on the measurement error covariance, but also on the model error covariance. Further analysis reveals that the optimal weight matrix can also make the ‘relative error’—square-root of expectation of squares error in every individual damage parameter minimized. Then, the effect of the proposed optimal weight matrix with measurement and/or model errors is studied on two typical examples—a plane frame and a simply-supported plate. Results show that when hybrid types of measurement data—accelerations, displacements and/or eigenfrequencies are used or when the response data is sensitive to model errors, the optimal weight matrix should be invoked to get reasonably good identification results and the improvements brought by the optimal weight matrix are substantial. The whole work shall be instructive for damage identification when different types of measurements are available and when model errors are non-negligible. Optimal weight matrix Structural damage identification Measurement and model errors Enhanced response sensitivity approach Hybrid data Zhou, Junxian verfasserin aut Wang, Li verfasserin (orcid)0000-0002-8556-5670 aut Enthalten in Mechanical systems and signal processing Amsterdam [u.a.] : Elsevier, 1987 114, Seite 1-24 Online-Ressource (DE-627)267838670 (DE-600)1471003-1 (DE-576)253127629 1096-1216 nnns volume:114 pages:1-24 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_101 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_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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.32 Dynamik Schwingungslehre Technische Mechanik 50.16 Technische Zuverlässigkeit Instandhaltung AR 114 1-24
allfields_unstemmed	10.1016/j.ymssp.2018.05.007 doi (DE-627)ELV002521466 (ELSEVIER)S0888-3270(18)30264-4 DE-627 ger DE-627 rda eng 004 DE-600 50.32 bkl 50.16 bkl Lu, Zhong-Rong verfasserin aut On choice and effect of weight matrix for response sensitivity-based damage identification with measurement and model errors 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper aims to present a thorough view on the choice and effect of the weight matrix for response sensitivity-based damage identification with measurement and/or model errors. The derivation of the optimal weight matrix is mainly twofold. On the one hand, when only measurement errors are involved, the optimal weight matrix is found to be inverse proportional to the measurement error covariance by minimizing the expectation of squares error of the whole identification results. On the other hand, if model errors are additionally considered, the optimal weight matrix then depends not only on the measurement error covariance, but also on the model error covariance. Further analysis reveals that the optimal weight matrix can also make the ‘relative error’—square-root of expectation of squares error in every individual damage parameter minimized. Then, the effect of the proposed optimal weight matrix with measurement and/or model errors is studied on two typical examples—a plane frame and a simply-supported plate. Results show that when hybrid types of measurement data—accelerations, displacements and/or eigenfrequencies are used or when the response data is sensitive to model errors, the optimal weight matrix should be invoked to get reasonably good identification results and the improvements brought by the optimal weight matrix are substantial. The whole work shall be instructive for damage identification when different types of measurements are available and when model errors are non-negligible. Optimal weight matrix Structural damage identification Measurement and model errors Enhanced response sensitivity approach Hybrid data Zhou, Junxian verfasserin aut Wang, Li verfasserin (orcid)0000-0002-8556-5670 aut Enthalten in Mechanical systems and signal processing Amsterdam [u.a.] : Elsevier, 1987 114, Seite 1-24 Online-Ressource (DE-627)267838670 (DE-600)1471003-1 (DE-576)253127629 1096-1216 nnns volume:114 pages:1-24 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_101 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_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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.32 Dynamik Schwingungslehre Technische Mechanik 50.16 Technische Zuverlässigkeit Instandhaltung AR 114 1-24
allfieldsGer	10.1016/j.ymssp.2018.05.007 doi (DE-627)ELV002521466 (ELSEVIER)S0888-3270(18)30264-4 DE-627 ger DE-627 rda eng 004 DE-600 50.32 bkl 50.16 bkl Lu, Zhong-Rong verfasserin aut On choice and effect of weight matrix for response sensitivity-based damage identification with measurement and model errors 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper aims to present a thorough view on the choice and effect of the weight matrix for response sensitivity-based damage identification with measurement and/or model errors. The derivation of the optimal weight matrix is mainly twofold. On the one hand, when only measurement errors are involved, the optimal weight matrix is found to be inverse proportional to the measurement error covariance by minimizing the expectation of squares error of the whole identification results. On the other hand, if model errors are additionally considered, the optimal weight matrix then depends not only on the measurement error covariance, but also on the model error covariance. Further analysis reveals that the optimal weight matrix can also make the ‘relative error’—square-root of expectation of squares error in every individual damage parameter minimized. Then, the effect of the proposed optimal weight matrix with measurement and/or model errors is studied on two typical examples—a plane frame and a simply-supported plate. Results show that when hybrid types of measurement data—accelerations, displacements and/or eigenfrequencies are used or when the response data is sensitive to model errors, the optimal weight matrix should be invoked to get reasonably good identification results and the improvements brought by the optimal weight matrix are substantial. The whole work shall be instructive for damage identification when different types of measurements are available and when model errors are non-negligible. Optimal weight matrix Structural damage identification Measurement and model errors Enhanced response sensitivity approach Hybrid data Zhou, Junxian verfasserin aut Wang, Li verfasserin (orcid)0000-0002-8556-5670 aut Enthalten in Mechanical systems and signal processing Amsterdam [u.a.] : Elsevier, 1987 114, Seite 1-24 Online-Ressource (DE-627)267838670 (DE-600)1471003-1 (DE-576)253127629 1096-1216 nnns volume:114 pages:1-24 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_101 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_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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.32 Dynamik Schwingungslehre Technische Mechanik 50.16 Technische Zuverlässigkeit Instandhaltung AR 114 1-24
allfieldsSound	10.1016/j.ymssp.2018.05.007 doi (DE-627)ELV002521466 (ELSEVIER)S0888-3270(18)30264-4 DE-627 ger DE-627 rda eng 004 DE-600 50.32 bkl 50.16 bkl Lu, Zhong-Rong verfasserin aut On choice and effect of weight matrix for response sensitivity-based damage identification with measurement and model errors 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper aims to present a thorough view on the choice and effect of the weight matrix for response sensitivity-based damage identification with measurement and/or model errors. The derivation of the optimal weight matrix is mainly twofold. On the one hand, when only measurement errors are involved, the optimal weight matrix is found to be inverse proportional to the measurement error covariance by minimizing the expectation of squares error of the whole identification results. On the other hand, if model errors are additionally considered, the optimal weight matrix then depends not only on the measurement error covariance, but also on the model error covariance. Further analysis reveals that the optimal weight matrix can also make the ‘relative error’—square-root of expectation of squares error in every individual damage parameter minimized. Then, the effect of the proposed optimal weight matrix with measurement and/or model errors is studied on two typical examples—a plane frame and a simply-supported plate. Results show that when hybrid types of measurement data—accelerations, displacements and/or eigenfrequencies are used or when the response data is sensitive to model errors, the optimal weight matrix should be invoked to get reasonably good identification results and the improvements brought by the optimal weight matrix are substantial. The whole work shall be instructive for damage identification when different types of measurements are available and when model errors are non-negligible. Optimal weight matrix Structural damage identification Measurement and model errors Enhanced response sensitivity approach Hybrid data Zhou, Junxian verfasserin aut Wang, Li verfasserin (orcid)0000-0002-8556-5670 aut Enthalten in Mechanical systems and signal processing Amsterdam [u.a.] : Elsevier, 1987 114, Seite 1-24 Online-Ressource (DE-627)267838670 (DE-600)1471003-1 (DE-576)253127629 1096-1216 nnns volume:114 pages:1-24 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_101 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_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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.32 Dynamik Schwingungslehre Technische Mechanik 50.16 Technische Zuverlässigkeit Instandhaltung AR 114 1-24
language	English
source	Enthalten in Mechanical systems and signal processing 114, Seite 1-24 volume:114 pages:1-24
sourceStr	Enthalten in Mechanical systems and signal processing 114, Seite 1-24 volume:114 pages:1-24
format_phy_str_mv	Article
bklname	Dynamik Schwingungslehre Technische Zuverlässigkeit Instandhaltung
institution	findex.gbv.de
topic_facet	Optimal weight matrix Structural damage identification Measurement and model errors Enhanced response sensitivity approach Hybrid data
dewey-raw	004
isfreeaccess_bool	false
container_title	Mechanical systems and signal processing
authorswithroles_txt_mv	Lu, Zhong-Rong @@aut@@ Zhou, Junxian @@aut@@ Wang, Li @@aut@@
publishDateDaySort_date	2018-01-01T00:00:00Z
hierarchy_top_id	267838670
dewey-sort	14
id	ELV002521466
language_de	englisch
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author	Lu, Zhong-Rong
spellingShingle	Lu, Zhong-Rong ddc 004 bkl 50.32 bkl 50.16 misc Optimal weight matrix misc Structural damage identification misc Measurement and model errors misc Enhanced response sensitivity approach misc Hybrid data On choice and effect of weight matrix for response sensitivity-based damage identification with measurement and model errors
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topic_title	004 DE-600 50.32 bkl 50.16 bkl On choice and effect of weight matrix for response sensitivity-based damage identification with measurement and model errors Optimal weight matrix Structural damage identification Measurement and model errors Enhanced response sensitivity approach Hybrid data
topic	ddc 004 bkl 50.32 bkl 50.16 misc Optimal weight matrix misc Structural damage identification misc Measurement and model errors misc Enhanced response sensitivity approach misc Hybrid data
topic_unstemmed	ddc 004 bkl 50.32 bkl 50.16 misc Optimal weight matrix misc Structural damage identification misc Measurement and model errors misc Enhanced response sensitivity approach misc Hybrid data
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title	On choice and effect of weight matrix for response sensitivity-based damage identification with measurement and model errors
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title_full	On choice and effect of weight matrix for response sensitivity-based damage identification with measurement and model errors
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title_sort	on choice and effect of weight matrix for response sensitivity-based damage identification with measurement and model errors
title_auth	On choice and effect of weight matrix for response sensitivity-based damage identification with measurement and model errors
abstract	This paper aims to present a thorough view on the choice and effect of the weight matrix for response sensitivity-based damage identification with measurement and/or model errors. The derivation of the optimal weight matrix is mainly twofold. On the one hand, when only measurement errors are involved, the optimal weight matrix is found to be inverse proportional to the measurement error covariance by minimizing the expectation of squares error of the whole identification results. On the other hand, if model errors are additionally considered, the optimal weight matrix then depends not only on the measurement error covariance, but also on the model error covariance. Further analysis reveals that the optimal weight matrix can also make the ‘relative error’—square-root of expectation of squares error in every individual damage parameter minimized. Then, the effect of the proposed optimal weight matrix with measurement and/or model errors is studied on two typical examples—a plane frame and a simply-supported plate. Results show that when hybrid types of measurement data—accelerations, displacements and/or eigenfrequencies are used or when the response data is sensitive to model errors, the optimal weight matrix should be invoked to get reasonably good identification results and the improvements brought by the optimal weight matrix are substantial. The whole work shall be instructive for damage identification when different types of measurements are available and when model errors are non-negligible.
abstractGer	This paper aims to present a thorough view on the choice and effect of the weight matrix for response sensitivity-based damage identification with measurement and/or model errors. The derivation of the optimal weight matrix is mainly twofold. On the one hand, when only measurement errors are involved, the optimal weight matrix is found to be inverse proportional to the measurement error covariance by minimizing the expectation of squares error of the whole identification results. On the other hand, if model errors are additionally considered, the optimal weight matrix then depends not only on the measurement error covariance, but also on the model error covariance. Further analysis reveals that the optimal weight matrix can also make the ‘relative error’—square-root of expectation of squares error in every individual damage parameter minimized. Then, the effect of the proposed optimal weight matrix with measurement and/or model errors is studied on two typical examples—a plane frame and a simply-supported plate. Results show that when hybrid types of measurement data—accelerations, displacements and/or eigenfrequencies are used or when the response data is sensitive to model errors, the optimal weight matrix should be invoked to get reasonably good identification results and the improvements brought by the optimal weight matrix are substantial. The whole work shall be instructive for damage identification when different types of measurements are available and when model errors are non-negligible.
abstract_unstemmed	This paper aims to present a thorough view on the choice and effect of the weight matrix for response sensitivity-based damage identification with measurement and/or model errors. The derivation of the optimal weight matrix is mainly twofold. On the one hand, when only measurement errors are involved, the optimal weight matrix is found to be inverse proportional to the measurement error covariance by minimizing the expectation of squares error of the whole identification results. On the other hand, if model errors are additionally considered, the optimal weight matrix then depends not only on the measurement error covariance, but also on the model error covariance. Further analysis reveals that the optimal weight matrix can also make the ‘relative error’—square-root of expectation of squares error in every individual damage parameter minimized. Then, the effect of the proposed optimal weight matrix with measurement and/or model errors is studied on two typical examples—a plane frame and a simply-supported plate. Results show that when hybrid types of measurement data—accelerations, displacements and/or eigenfrequencies are used or when the response data is sensitive to model errors, the optimal weight matrix should be invoked to get reasonably good identification results and the improvements brought by the optimal weight matrix are substantial. The whole work shall be instructive for damage identification when different types of measurements are available and when model errors are non-negligible.
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title_short	On choice and effect of weight matrix for response sensitivity-based damage identification with measurement and model errors
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doi_str	10.1016/j.ymssp.2018.05.007
up_date	2024-07-06T16:33:04.323Z
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On choice and effect of weight matrix for response sensitivity-based damage identification with measurement and model errors

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