Output-Only Modal Identification Based on Auto-regressive Spectrum-Guided Symplectic Geometry Mode Decomposition

Purpose The purpose of this paper is to propose a novel output-only structural modal identification approach based on an improved symplectic geometry mode decomposition (SGMD) method, i.e., auto-regressive (AR) spectrum-guided symplectic geometry mode decomposition (ARSGMD). Therefore, output-only modal identification for structures based on the signal decomposition technique is studied. Methods First, we improved the SGMD by introducing the AR spectrum to determine the number of iterations and the frequency bound for each iteration of SGMD, and the dynamic response is decomposed into several symplectic geometry components (SGCs) containing one major frequency by the ARSGMD. Then, the free decay response (FDR) can be obtained by the random decrement technique (RDT) and the mode shape can be extracted by the modal responses extracted from the FDRs at all available sensors. Finally, the natural frequencies and damping ratio can be estimated by the Hilbert transformation (HT). Results The performance and superiority of the proposed ARSGMD method is validated by numerical example and compare with others methods. The result shows that the ARSGMD has better ability in signal decomposition for structural modal identification. The feasibility of the proposed ARSGMD-based output-only modal identification approach is demonstrated by the numerical example and experimental investigation. The results show that the proposed method can identify the modal parameters accurately. Conclusions Associate with the AR spectrum, the proposed ARSGMD approach can decompose the complex signal into several single frequency components adaptively without mode mixing and over-decomposition, even for noise-contaminated signal. The proposed ARSGMD-based modal identification method can effectively extract the structural modal parameters under impulse or ambient excitation. Besides, the closely spaced modes can be accurately decomposed and extracted through the proposed ARSGMD-based modal identification approach. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Zhan, Pengming [verfasserIn] Qin, Xianrong Zhang, Qing Sun, Yuantao

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Modal identification Symplectic geometry mode decomposition Auto-regressive power spectrum Structural health monitoring Signal decomposition

Anmerkung:	© Krishtel eMaging Solutions Private Limited 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Journal of vibration engineering & technologies - Singapore : Springer Singapore, 2018, 12(2023), 1 vom: 03. Jan., Seite 139-161
Übergeordnetes Werk:	volume:12 ; year:2023 ; number:1 ; day:03 ; month:01 ; pages:139-161

Links:	Volltext

DOI / URN:	10.1007/s42417-022-00832-1

Katalog-ID:	SPR054642841

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520			\|a Purpose The purpose of this paper is to propose a novel output-only structural modal identification approach based on an improved symplectic geometry mode decomposition (SGMD) method, i.e., auto-regressive (AR) spectrum-guided symplectic geometry mode decomposition (ARSGMD). Therefore, output-only modal identification for structures based on the signal decomposition technique is studied. Methods First, we improved the SGMD by introducing the AR spectrum to determine the number of iterations and the frequency bound for each iteration of SGMD, and the dynamic response is decomposed into several symplectic geometry components (SGCs) containing one major frequency by the ARSGMD. Then, the free decay response (FDR) can be obtained by the random decrement technique (RDT) and the mode shape can be extracted by the modal responses extracted from the FDRs at all available sensors. Finally, the natural frequencies and damping ratio can be estimated by the Hilbert transformation (HT). Results The performance and superiority of the proposed ARSGMD method is validated by numerical example and compare with others methods. The result shows that the ARSGMD has better ability in signal decomposition for structural modal identification. The feasibility of the proposed ARSGMD-based output-only modal identification approach is demonstrated by the numerical example and experimental investigation. The results show that the proposed method can identify the modal parameters accurately. Conclusions Associate with the AR spectrum, the proposed ARSGMD approach can decompose the complex signal into several single frequency components adaptively without mode mixing and over-decomposition, even for noise-contaminated signal. The proposed ARSGMD-based modal identification method can effectively extract the structural modal parameters under impulse or ambient excitation. Besides, the closely spaced modes can be accurately decomposed and extracted through the proposed ARSGMD-based modal identification approach.
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allfields	10.1007/s42417-022-00832-1 doi (DE-627)SPR054642841 (SPR)s42417-022-00832-1-e DE-627 ger DE-627 rakwb eng Zhan, Pengming verfasserin (orcid)0000-0001-6018-5893 aut Output-Only Modal Identification Based on Auto-regressive Spectrum-Guided Symplectic Geometry Mode Decomposition 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Krishtel eMaging Solutions Private Limited 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Purpose The purpose of this paper is to propose a novel output-only structural modal identification approach based on an improved symplectic geometry mode decomposition (SGMD) method, i.e., auto-regressive (AR) spectrum-guided symplectic geometry mode decomposition (ARSGMD). Therefore, output-only modal identification for structures based on the signal decomposition technique is studied. Methods First, we improved the SGMD by introducing the AR spectrum to determine the number of iterations and the frequency bound for each iteration of SGMD, and the dynamic response is decomposed into several symplectic geometry components (SGCs) containing one major frequency by the ARSGMD. Then, the free decay response (FDR) can be obtained by the random decrement technique (RDT) and the mode shape can be extracted by the modal responses extracted from the FDRs at all available sensors. Finally, the natural frequencies and damping ratio can be estimated by the Hilbert transformation (HT). Results The performance and superiority of the proposed ARSGMD method is validated by numerical example and compare with others methods. The result shows that the ARSGMD has better ability in signal decomposition for structural modal identification. The feasibility of the proposed ARSGMD-based output-only modal identification approach is demonstrated by the numerical example and experimental investigation. The results show that the proposed method can identify the modal parameters accurately. Conclusions Associate with the AR spectrum, the proposed ARSGMD approach can decompose the complex signal into several single frequency components adaptively without mode mixing and over-decomposition, even for noise-contaminated signal. The proposed ARSGMD-based modal identification method can effectively extract the structural modal parameters under impulse or ambient excitation. Besides, the closely spaced modes can be accurately decomposed and extracted through the proposed ARSGMD-based modal identification approach. Modal identification (dpeaa)DE-He213 Symplectic geometry mode decomposition (dpeaa)DE-He213 Auto-regressive power spectrum (dpeaa)DE-He213 Structural health monitoring (dpeaa)DE-He213 Signal decomposition (dpeaa)DE-He213 Qin, Xianrong (orcid)0000-0002-5792-5704 aut Zhang, Qing aut Sun, Yuantao aut Enthalten in Journal of vibration engineering & technologies Singapore : Springer Singapore, 2018 12(2023), 1 vom: 03. Jan., Seite 139-161 (DE-627)1030123837 (DE-600)2941414-3 2523-3939 nnns volume:12 year:2023 number:1 day:03 month:01 pages:139-161 https://dx.doi.org/10.1007/s42417-022-00832-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2023 1 03 01 139-161
spelling	10.1007/s42417-022-00832-1 doi (DE-627)SPR054642841 (SPR)s42417-022-00832-1-e DE-627 ger DE-627 rakwb eng Zhan, Pengming verfasserin (orcid)0000-0001-6018-5893 aut Output-Only Modal Identification Based on Auto-regressive Spectrum-Guided Symplectic Geometry Mode Decomposition 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Krishtel eMaging Solutions Private Limited 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Purpose The purpose of this paper is to propose a novel output-only structural modal identification approach based on an improved symplectic geometry mode decomposition (SGMD) method, i.e., auto-regressive (AR) spectrum-guided symplectic geometry mode decomposition (ARSGMD). Therefore, output-only modal identification for structures based on the signal decomposition technique is studied. Methods First, we improved the SGMD by introducing the AR spectrum to determine the number of iterations and the frequency bound for each iteration of SGMD, and the dynamic response is decomposed into several symplectic geometry components (SGCs) containing one major frequency by the ARSGMD. Then, the free decay response (FDR) can be obtained by the random decrement technique (RDT) and the mode shape can be extracted by the modal responses extracted from the FDRs at all available sensors. Finally, the natural frequencies and damping ratio can be estimated by the Hilbert transformation (HT). Results The performance and superiority of the proposed ARSGMD method is validated by numerical example and compare with others methods. The result shows that the ARSGMD has better ability in signal decomposition for structural modal identification. The feasibility of the proposed ARSGMD-based output-only modal identification approach is demonstrated by the numerical example and experimental investigation. The results show that the proposed method can identify the modal parameters accurately. Conclusions Associate with the AR spectrum, the proposed ARSGMD approach can decompose the complex signal into several single frequency components adaptively without mode mixing and over-decomposition, even for noise-contaminated signal. The proposed ARSGMD-based modal identification method can effectively extract the structural modal parameters under impulse or ambient excitation. Besides, the closely spaced modes can be accurately decomposed and extracted through the proposed ARSGMD-based modal identification approach. Modal identification (dpeaa)DE-He213 Symplectic geometry mode decomposition (dpeaa)DE-He213 Auto-regressive power spectrum (dpeaa)DE-He213 Structural health monitoring (dpeaa)DE-He213 Signal decomposition (dpeaa)DE-He213 Qin, Xianrong (orcid)0000-0002-5792-5704 aut Zhang, Qing aut Sun, Yuantao aut Enthalten in Journal of vibration engineering & technologies Singapore : Springer Singapore, 2018 12(2023), 1 vom: 03. Jan., Seite 139-161 (DE-627)1030123837 (DE-600)2941414-3 2523-3939 nnns volume:12 year:2023 number:1 day:03 month:01 pages:139-161 https://dx.doi.org/10.1007/s42417-022-00832-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2023 1 03 01 139-161
allfields_unstemmed	10.1007/s42417-022-00832-1 doi (DE-627)SPR054642841 (SPR)s42417-022-00832-1-e DE-627 ger DE-627 rakwb eng Zhan, Pengming verfasserin (orcid)0000-0001-6018-5893 aut Output-Only Modal Identification Based on Auto-regressive Spectrum-Guided Symplectic Geometry Mode Decomposition 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Krishtel eMaging Solutions Private Limited 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Purpose The purpose of this paper is to propose a novel output-only structural modal identification approach based on an improved symplectic geometry mode decomposition (SGMD) method, i.e., auto-regressive (AR) spectrum-guided symplectic geometry mode decomposition (ARSGMD). Therefore, output-only modal identification for structures based on the signal decomposition technique is studied. Methods First, we improved the SGMD by introducing the AR spectrum to determine the number of iterations and the frequency bound for each iteration of SGMD, and the dynamic response is decomposed into several symplectic geometry components (SGCs) containing one major frequency by the ARSGMD. Then, the free decay response (FDR) can be obtained by the random decrement technique (RDT) and the mode shape can be extracted by the modal responses extracted from the FDRs at all available sensors. Finally, the natural frequencies and damping ratio can be estimated by the Hilbert transformation (HT). Results The performance and superiority of the proposed ARSGMD method is validated by numerical example and compare with others methods. The result shows that the ARSGMD has better ability in signal decomposition for structural modal identification. The feasibility of the proposed ARSGMD-based output-only modal identification approach is demonstrated by the numerical example and experimental investigation. The results show that the proposed method can identify the modal parameters accurately. Conclusions Associate with the AR spectrum, the proposed ARSGMD approach can decompose the complex signal into several single frequency components adaptively without mode mixing and over-decomposition, even for noise-contaminated signal. The proposed ARSGMD-based modal identification method can effectively extract the structural modal parameters under impulse or ambient excitation. Besides, the closely spaced modes can be accurately decomposed and extracted through the proposed ARSGMD-based modal identification approach. Modal identification (dpeaa)DE-He213 Symplectic geometry mode decomposition (dpeaa)DE-He213 Auto-regressive power spectrum (dpeaa)DE-He213 Structural health monitoring (dpeaa)DE-He213 Signal decomposition (dpeaa)DE-He213 Qin, Xianrong (orcid)0000-0002-5792-5704 aut Zhang, Qing aut Sun, Yuantao aut Enthalten in Journal of vibration engineering & technologies Singapore : Springer Singapore, 2018 12(2023), 1 vom: 03. Jan., Seite 139-161 (DE-627)1030123837 (DE-600)2941414-3 2523-3939 nnns volume:12 year:2023 number:1 day:03 month:01 pages:139-161 https://dx.doi.org/10.1007/s42417-022-00832-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2023 1 03 01 139-161
allfieldsGer	10.1007/s42417-022-00832-1 doi (DE-627)SPR054642841 (SPR)s42417-022-00832-1-e DE-627 ger DE-627 rakwb eng Zhan, Pengming verfasserin (orcid)0000-0001-6018-5893 aut Output-Only Modal Identification Based on Auto-regressive Spectrum-Guided Symplectic Geometry Mode Decomposition 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Krishtel eMaging Solutions Private Limited 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Purpose The purpose of this paper is to propose a novel output-only structural modal identification approach based on an improved symplectic geometry mode decomposition (SGMD) method, i.e., auto-regressive (AR) spectrum-guided symplectic geometry mode decomposition (ARSGMD). Therefore, output-only modal identification for structures based on the signal decomposition technique is studied. Methods First, we improved the SGMD by introducing the AR spectrum to determine the number of iterations and the frequency bound for each iteration of SGMD, and the dynamic response is decomposed into several symplectic geometry components (SGCs) containing one major frequency by the ARSGMD. Then, the free decay response (FDR) can be obtained by the random decrement technique (RDT) and the mode shape can be extracted by the modal responses extracted from the FDRs at all available sensors. Finally, the natural frequencies and damping ratio can be estimated by the Hilbert transformation (HT). Results The performance and superiority of the proposed ARSGMD method is validated by numerical example and compare with others methods. The result shows that the ARSGMD has better ability in signal decomposition for structural modal identification. The feasibility of the proposed ARSGMD-based output-only modal identification approach is demonstrated by the numerical example and experimental investigation. The results show that the proposed method can identify the modal parameters accurately. Conclusions Associate with the AR spectrum, the proposed ARSGMD approach can decompose the complex signal into several single frequency components adaptively without mode mixing and over-decomposition, even for noise-contaminated signal. The proposed ARSGMD-based modal identification method can effectively extract the structural modal parameters under impulse or ambient excitation. Besides, the closely spaced modes can be accurately decomposed and extracted through the proposed ARSGMD-based modal identification approach. Modal identification (dpeaa)DE-He213 Symplectic geometry mode decomposition (dpeaa)DE-He213 Auto-regressive power spectrum (dpeaa)DE-He213 Structural health monitoring (dpeaa)DE-He213 Signal decomposition (dpeaa)DE-He213 Qin, Xianrong (orcid)0000-0002-5792-5704 aut Zhang, Qing aut Sun, Yuantao aut Enthalten in Journal of vibration engineering & technologies Singapore : Springer Singapore, 2018 12(2023), 1 vom: 03. Jan., Seite 139-161 (DE-627)1030123837 (DE-600)2941414-3 2523-3939 nnns volume:12 year:2023 number:1 day:03 month:01 pages:139-161 https://dx.doi.org/10.1007/s42417-022-00832-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2023 1 03 01 139-161
allfieldsSound	10.1007/s42417-022-00832-1 doi (DE-627)SPR054642841 (SPR)s42417-022-00832-1-e DE-627 ger DE-627 rakwb eng Zhan, Pengming verfasserin (orcid)0000-0001-6018-5893 aut Output-Only Modal Identification Based on Auto-regressive Spectrum-Guided Symplectic Geometry Mode Decomposition 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Krishtel eMaging Solutions Private Limited 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Purpose The purpose of this paper is to propose a novel output-only structural modal identification approach based on an improved symplectic geometry mode decomposition (SGMD) method, i.e., auto-regressive (AR) spectrum-guided symplectic geometry mode decomposition (ARSGMD). Therefore, output-only modal identification for structures based on the signal decomposition technique is studied. Methods First, we improved the SGMD by introducing the AR spectrum to determine the number of iterations and the frequency bound for each iteration of SGMD, and the dynamic response is decomposed into several symplectic geometry components (SGCs) containing one major frequency by the ARSGMD. Then, the free decay response (FDR) can be obtained by the random decrement technique (RDT) and the mode shape can be extracted by the modal responses extracted from the FDRs at all available sensors. Finally, the natural frequencies and damping ratio can be estimated by the Hilbert transformation (HT). Results The performance and superiority of the proposed ARSGMD method is validated by numerical example and compare with others methods. The result shows that the ARSGMD has better ability in signal decomposition for structural modal identification. The feasibility of the proposed ARSGMD-based output-only modal identification approach is demonstrated by the numerical example and experimental investigation. The results show that the proposed method can identify the modal parameters accurately. Conclusions Associate with the AR spectrum, the proposed ARSGMD approach can decompose the complex signal into several single frequency components adaptively without mode mixing and over-decomposition, even for noise-contaminated signal. The proposed ARSGMD-based modal identification method can effectively extract the structural modal parameters under impulse or ambient excitation. Besides, the closely spaced modes can be accurately decomposed and extracted through the proposed ARSGMD-based modal identification approach. Modal identification (dpeaa)DE-He213 Symplectic geometry mode decomposition (dpeaa)DE-He213 Auto-regressive power spectrum (dpeaa)DE-He213 Structural health monitoring (dpeaa)DE-He213 Signal decomposition (dpeaa)DE-He213 Qin, Xianrong (orcid)0000-0002-5792-5704 aut Zhang, Qing aut Sun, Yuantao aut Enthalten in Journal of vibration engineering & technologies Singapore : Springer Singapore, 2018 12(2023), 1 vom: 03. Jan., Seite 139-161 (DE-627)1030123837 (DE-600)2941414-3 2523-3939 nnns volume:12 year:2023 number:1 day:03 month:01 pages:139-161 https://dx.doi.org/10.1007/s42417-022-00832-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 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_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 12 2023 1 03 01 139-161
language	English
source	Enthalten in Journal of vibration engineering & technologies 12(2023), 1 vom: 03. Jan., Seite 139-161 volume:12 year:2023 number:1 day:03 month:01 pages:139-161
sourceStr	Enthalten in Journal of vibration engineering & technologies 12(2023), 1 vom: 03. Jan., Seite 139-161 volume:12 year:2023 number:1 day:03 month:01 pages:139-161
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Modal identification Symplectic geometry mode decomposition Auto-regressive power spectrum Structural health monitoring Signal decomposition
isfreeaccess_bool	false
container_title	Journal of vibration engineering & technologies
authorswithroles_txt_mv	Zhan, Pengming @@aut@@ Qin, Xianrong @@aut@@ Zhang, Qing @@aut@@ Sun, Yuantao @@aut@@
publishDateDaySort_date	2023-01-03T00:00:00Z
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Purpose The purpose of this paper is to propose a novel output-only structural modal identification approach based on an improved symplectic geometry mode decomposition (SGMD) method, i.e., auto-regressive (AR) spectrum-guided symplectic geometry mode decomposition (ARSGMD). Therefore, output-only modal identification for structures based on the signal decomposition technique is studied. Methods First, we improved the SGMD by introducing the AR spectrum to determine the number of iterations and the frequency bound for each iteration of SGMD, and the dynamic response is decomposed into several symplectic geometry components (SGCs) containing one major frequency by the ARSGMD. Then, the free decay response (FDR) can be obtained by the random decrement technique (RDT) and the mode shape can be extracted by the modal responses extracted from the FDRs at all available sensors. Finally, the natural frequencies and damping ratio can be estimated by the Hilbert transformation (HT). Results The performance and superiority of the proposed ARSGMD method is validated by numerical example and compare with others methods. The result shows that the ARSGMD has better ability in signal decomposition for structural modal identification. The feasibility of the proposed ARSGMD-based output-only modal identification approach is demonstrated by the numerical example and experimental investigation. The results show that the proposed method can identify the modal parameters accurately. Conclusions Associate with the AR spectrum, the proposed ARSGMD approach can decompose the complex signal into several single frequency components adaptively without mode mixing and over-decomposition, even for noise-contaminated signal. The proposed ARSGMD-based modal identification method can effectively extract the structural modal parameters under impulse or ambient excitation. 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author	Zhan, Pengming
spellingShingle	Zhan, Pengming misc Modal identification misc Symplectic geometry mode decomposition misc Auto-regressive power spectrum misc Structural health monitoring misc Signal decomposition Output-Only Modal Identification Based on Auto-regressive Spectrum-Guided Symplectic Geometry Mode Decomposition
authorStr	Zhan, Pengming
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topic_title	Output-Only Modal Identification Based on Auto-regressive Spectrum-Guided Symplectic Geometry Mode Decomposition Modal identification (dpeaa)DE-He213 Symplectic geometry mode decomposition (dpeaa)DE-He213 Auto-regressive power spectrum (dpeaa)DE-He213 Structural health monitoring (dpeaa)DE-He213 Signal decomposition (dpeaa)DE-He213
topic	misc Modal identification misc Symplectic geometry mode decomposition misc Auto-regressive power spectrum misc Structural health monitoring misc Signal decomposition
topic_unstemmed	misc Modal identification misc Symplectic geometry mode decomposition misc Auto-regressive power spectrum misc Structural health monitoring misc Signal decomposition
topic_browse	misc Modal identification misc Symplectic geometry mode decomposition misc Auto-regressive power spectrum misc Structural health monitoring misc Signal decomposition
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
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title	Output-Only Modal Identification Based on Auto-regressive Spectrum-Guided Symplectic Geometry Mode Decomposition
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title_full	Output-Only Modal Identification Based on Auto-regressive Spectrum-Guided Symplectic Geometry Mode Decomposition
author_sort	Zhan, Pengming
journal	Journal of vibration engineering & technologies
journalStr	Journal of vibration engineering & technologies
lang_code	eng
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container_start_page	139
author_browse	Zhan, Pengming Qin, Xianrong Zhang, Qing Sun, Yuantao
container_volume	12
format_se	Elektronische Aufsätze
author-letter	Zhan, Pengming
doi_str_mv	10.1007/s42417-022-00832-1
normlink	(ORCID)0000-0001-6018-5893 (ORCID)0000-0002-5792-5704
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title_sort	output-only modal identification based on auto-regressive spectrum-guided symplectic geometry mode decomposition
title_auth	Output-Only Modal Identification Based on Auto-regressive Spectrum-Guided Symplectic Geometry Mode Decomposition
abstract	Purpose The purpose of this paper is to propose a novel output-only structural modal identification approach based on an improved symplectic geometry mode decomposition (SGMD) method, i.e., auto-regressive (AR) spectrum-guided symplectic geometry mode decomposition (ARSGMD). Therefore, output-only modal identification for structures based on the signal decomposition technique is studied. Methods First, we improved the SGMD by introducing the AR spectrum to determine the number of iterations and the frequency bound for each iteration of SGMD, and the dynamic response is decomposed into several symplectic geometry components (SGCs) containing one major frequency by the ARSGMD. Then, the free decay response (FDR) can be obtained by the random decrement technique (RDT) and the mode shape can be extracted by the modal responses extracted from the FDRs at all available sensors. Finally, the natural frequencies and damping ratio can be estimated by the Hilbert transformation (HT). Results The performance and superiority of the proposed ARSGMD method is validated by numerical example and compare with others methods. The result shows that the ARSGMD has better ability in signal decomposition for structural modal identification. The feasibility of the proposed ARSGMD-based output-only modal identification approach is demonstrated by the numerical example and experimental investigation. The results show that the proposed method can identify the modal parameters accurately. Conclusions Associate with the AR spectrum, the proposed ARSGMD approach can decompose the complex signal into several single frequency components adaptively without mode mixing and over-decomposition, even for noise-contaminated signal. The proposed ARSGMD-based modal identification method can effectively extract the structural modal parameters under impulse or ambient excitation. Besides, the closely spaced modes can be accurately decomposed and extracted through the proposed ARSGMD-based modal identification approach. © Krishtel eMaging Solutions Private Limited 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Purpose The purpose of this paper is to propose a novel output-only structural modal identification approach based on an improved symplectic geometry mode decomposition (SGMD) method, i.e., auto-regressive (AR) spectrum-guided symplectic geometry mode decomposition (ARSGMD). Therefore, output-only modal identification for structures based on the signal decomposition technique is studied. Methods First, we improved the SGMD by introducing the AR spectrum to determine the number of iterations and the frequency bound for each iteration of SGMD, and the dynamic response is decomposed into several symplectic geometry components (SGCs) containing one major frequency by the ARSGMD. Then, the free decay response (FDR) can be obtained by the random decrement technique (RDT) and the mode shape can be extracted by the modal responses extracted from the FDRs at all available sensors. Finally, the natural frequencies and damping ratio can be estimated by the Hilbert transformation (HT). Results The performance and superiority of the proposed ARSGMD method is validated by numerical example and compare with others methods. The result shows that the ARSGMD has better ability in signal decomposition for structural modal identification. The feasibility of the proposed ARSGMD-based output-only modal identification approach is demonstrated by the numerical example and experimental investigation. The results show that the proposed method can identify the modal parameters accurately. Conclusions Associate with the AR spectrum, the proposed ARSGMD approach can decompose the complex signal into several single frequency components adaptively without mode mixing and over-decomposition, even for noise-contaminated signal. The proposed ARSGMD-based modal identification method can effectively extract the structural modal parameters under impulse or ambient excitation. Besides, the closely spaced modes can be accurately decomposed and extracted through the proposed ARSGMD-based modal identification approach. © Krishtel eMaging Solutions Private Limited 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Purpose The purpose of this paper is to propose a novel output-only structural modal identification approach based on an improved symplectic geometry mode decomposition (SGMD) method, i.e., auto-regressive (AR) spectrum-guided symplectic geometry mode decomposition (ARSGMD). Therefore, output-only modal identification for structures based on the signal decomposition technique is studied. Methods First, we improved the SGMD by introducing the AR spectrum to determine the number of iterations and the frequency bound for each iteration of SGMD, and the dynamic response is decomposed into several symplectic geometry components (SGCs) containing one major frequency by the ARSGMD. Then, the free decay response (FDR) can be obtained by the random decrement technique (RDT) and the mode shape can be extracted by the modal responses extracted from the FDRs at all available sensors. Finally, the natural frequencies and damping ratio can be estimated by the Hilbert transformation (HT). Results The performance and superiority of the proposed ARSGMD method is validated by numerical example and compare with others methods. The result shows that the ARSGMD has better ability in signal decomposition for structural modal identification. The feasibility of the proposed ARSGMD-based output-only modal identification approach is demonstrated by the numerical example and experimental investigation. The results show that the proposed method can identify the modal parameters accurately. Conclusions Associate with the AR spectrum, the proposed ARSGMD approach can decompose the complex signal into several single frequency components adaptively without mode mixing and over-decomposition, even for noise-contaminated signal. The proposed ARSGMD-based modal identification method can effectively extract the structural modal parameters under impulse or ambient excitation. Besides, the closely spaced modes can be accurately decomposed and extracted through the proposed ARSGMD-based modal identification approach. © Krishtel eMaging Solutions Private Limited 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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container_issue	1
title_short	Output-Only Modal Identification Based on Auto-regressive Spectrum-Guided Symplectic Geometry Mode Decomposition
url	https://dx.doi.org/10.1007/s42417-022-00832-1
remote_bool	true
author2	Qin, Xianrong Zhang, Qing Sun, Yuantao
author2Str	Qin, Xianrong Zhang, Qing Sun, Yuantao
ppnlink	1030123837
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hochschulschrift_bool	false
doi_str	10.1007/s42417-022-00832-1
up_date	2024-07-04T02:29:15.477Z
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Purpose The purpose of this paper is to propose a novel output-only structural modal identification approach based on an improved symplectic geometry mode decomposition (SGMD) method, i.e., auto-regressive (AR) spectrum-guided symplectic geometry mode decomposition (ARSGMD). Therefore, output-only modal identification for structures based on the signal decomposition technique is studied. Methods First, we improved the SGMD by introducing the AR spectrum to determine the number of iterations and the frequency bound for each iteration of SGMD, and the dynamic response is decomposed into several symplectic geometry components (SGCs) containing one major frequency by the ARSGMD. Then, the free decay response (FDR) can be obtained by the random decrement technique (RDT) and the mode shape can be extracted by the modal responses extracted from the FDRs at all available sensors. Finally, the natural frequencies and damping ratio can be estimated by the Hilbert transformation (HT). Results The performance and superiority of the proposed ARSGMD method is validated by numerical example and compare with others methods. The result shows that the ARSGMD has better ability in signal decomposition for structural modal identification. The feasibility of the proposed ARSGMD-based output-only modal identification approach is demonstrated by the numerical example and experimental investigation. The results show that the proposed method can identify the modal parameters accurately. Conclusions Associate with the AR spectrum, the proposed ARSGMD approach can decompose the complex signal into several single frequency components adaptively without mode mixing and over-decomposition, even for noise-contaminated signal. The proposed ARSGMD-based modal identification method can effectively extract the structural modal parameters under impulse or ambient excitation. 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Output-Only Modal Identification Based on Auto-regressive Spectrum-Guided Symplectic Geometry Mode Decomposition

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