Mechanics‐based model updating for identification and virtual sensing of an offshore wind turbine using sparse measurements
Summary Offshore wind turbines are complex systems operating in harsh environment. The dynamic demands in these systems often differ from values used in design, leading to unexpected mechanical and structural failures. This signifies the importance of remote monitoring technologies for damage diagno...
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| Published in: | Structural control and health monitoring Vol. 28; no. 2 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Pavia
John Wiley & Sons, Inc
01.02.2021
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| ISSN: | 1545-2255, 1545-2263 |
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| Abstract | Summary
Offshore wind turbines are complex systems operating in harsh environment. The dynamic demands in these systems often differ from values used in design, leading to unexpected mechanical and structural failures. This signifies the importance of remote monitoring technologies for damage diagnosis and prognosis in offshore wind turbines. This study is focused on developing mechanics‐based digital twins for offshore wind turbine monitoring through a model‐updating process using sparse measurement data. Digital twins can be used to estimate the system unmeasured response (i.e., virtual sensing) and to predict the remaining useful fatigue life and failure point of different structural components. A time‐domain sequential Bayesian finite element model updating is proposed for mechanics‐based digital twinning. This approach is formulated for application to offshore wind turbine and jointly estimates the updating model parameters and the time history of unknown input forces. A classical modal‐based model updating followed by modal expansion method is also implemented for comparison. In this approach, updating model parameters are estimated to minimize the discrepancies between the identified and model‐predicted modal parameters of the turbine. The performance of these two approaches are studied on a 2‐MW offshore wind turbine at the Blyth wind farm in the United Kingdom. Strain response time history at mudline is estimated through both approaches and compared with actual measurements for validation. It is observed that both approaches are capable of accurate response prediction while the Bayesian approach leads to slightly better results. Furthermore, the Bayesian approach allows for identification of input loads and uncertainty quantification. |
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| AbstractList | Offshore wind turbines are complex systems operating in harsh environment. The dynamic demands in these systems often differ from values used in design, leading to unexpected mechanical and structural failures. This signifies the importance of remote monitoring technologies for damage diagnosis and prognosis in offshore wind turbines. This study is focused on developing mechanics‐based digital twins for offshore wind turbine monitoring through a model‐updating process using sparse measurement data. Digital twins can be used to estimate the system unmeasured response (i.e., virtual sensing) and to predict the remaining useful fatigue life and failure point of different structural components. A time‐domain sequential Bayesian finite element model updating is proposed for mechanics‐based digital twinning. This approach is formulated for application to offshore wind turbine and jointly estimates the updating model parameters and the time history of unknown input forces. A classical modal‐based model updating followed by modal expansion method is also implemented for comparison. In this approach, updating model parameters are estimated to minimize the discrepancies between the identified and model‐predicted modal parameters of the turbine. The performance of these two approaches are studied on a 2‐MW offshore wind turbine at the Blyth wind farm in the United Kingdom. Strain response time history at mudline is estimated through both approaches and compared with actual measurements for validation. It is observed that both approaches are capable of accurate response prediction while the Bayesian approach leads to slightly better results. Furthermore, the Bayesian approach allows for identification of input loads and uncertainty quantification. Summary Offshore wind turbines are complex systems operating in harsh environment. The dynamic demands in these systems often differ from values used in design, leading to unexpected mechanical and structural failures. This signifies the importance of remote monitoring technologies for damage diagnosis and prognosis in offshore wind turbines. This study is focused on developing mechanics‐based digital twins for offshore wind turbine monitoring through a model‐updating process using sparse measurement data. Digital twins can be used to estimate the system unmeasured response (i.e., virtual sensing) and to predict the remaining useful fatigue life and failure point of different structural components. A time‐domain sequential Bayesian finite element model updating is proposed for mechanics‐based digital twinning. This approach is formulated for application to offshore wind turbine and jointly estimates the updating model parameters and the time history of unknown input forces. A classical modal‐based model updating followed by modal expansion method is also implemented for comparison. In this approach, updating model parameters are estimated to minimize the discrepancies between the identified and model‐predicted modal parameters of the turbine. The performance of these two approaches are studied on a 2‐MW offshore wind turbine at the Blyth wind farm in the United Kingdom. Strain response time history at mudline is estimated through both approaches and compared with actual measurements for validation. It is observed that both approaches are capable of accurate response prediction while the Bayesian approach leads to slightly better results. Furthermore, the Bayesian approach allows for identification of input loads and uncertainty quantification. |
| Author | Moaveni, Babak Nabiyan, Mansureh‐Sadat Khoshnoudian, Faramarz Ebrahimian, Hamed |
| Author_xml | – sequence: 1 givenname: Mansureh‐Sadat orcidid: 0000-0003-2287-4625 surname: Nabiyan fullname: Nabiyan, Mansureh‐Sadat organization: Amirkabir University of Technology – sequence: 2 givenname: Faramarz surname: Khoshnoudian fullname: Khoshnoudian, Faramarz email: khoshnud@aut.ac.ir organization: Amirkabir University of Technology – sequence: 3 givenname: Babak orcidid: 0000-0002-8462-4608 surname: Moaveni fullname: Moaveni, Babak organization: Tufts University – sequence: 4 givenname: Hamed orcidid: 0000-0003-1992-6033 surname: Ebrahimian fullname: Ebrahimian, Hamed organization: University of Nevada |
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| References | 2002; 16 2015; 283 2015; 15 2016; 19 2019; 2 2008; 19 2015; 74 1985; 8 1995; 10 2016; 76 1997 2019; 19 2007 2019; 18 2006 2016; 94 2005 2003 2013; 120 2018; 21 2011; 18 2015; 7 2017; 136 2017; 9 2018; 25 2014; 21 2017; 93 2016; 3 2013; 16 2000 2018 2017 2012; 29 2020; 23 2015 2014; 140 2014 2007; 43 2016; 68 e_1_2_8_28_1 (e_1_2_8_33_1) 2014 Astroza R (e_1_2_8_31_1) 2017 e_1_2_8_24_1 Iliopoulos A (e_1_2_8_8_1) 2016 e_1_2_8_25_1 e_1_2_8_46_1 e_1_2_8_49_1 e_1_2_8_27_1 e_1_2_8_48_1 Baqersad J (e_1_2_8_30_1) 2017 Manolakis DG (e_1_2_8_44_1) 2000 Arany L (e_1_2_8_36_1) 2015; 74 Fallais D (e_1_2_8_22_1) 2016; 94 e_1_2_8_3_1 e_1_2_8_2_1 James G (e_1_2_8_26_1) 1995; 10 e_1_2_8_5_1 e_1_2_8_7_1 e_1_2_8_9_1 Chopra AK (e_1_2_8_29_1) 2017 e_1_2_8_43_1 e_1_2_8_42_1 e_1_2_8_45_1 e_1_2_8_23_1 Valamanesh V (e_1_2_8_40_1) 2014; 140 e_1_2_8_41_1 e_1_2_8_17_1 e_1_2_8_18_1 e_1_2_8_39_1 Nabiyan M (e_1_2_8_6_1) 2019 e_1_2_8_19_1 e_1_2_8_13_1 Ciang CC (e_1_2_8_4_1) 2008; 19 e_1_2_8_14_1 e_1_2_8_35_1 e_1_2_8_38_1 e_1_2_8_16_1 e_1_2_8_37_1 Van der Male P (e_1_2_8_15_1) 2015 Chatzi EN (e_1_2_8_21_1) 2014 Xu Y (e_1_2_8_20_1) 2019; 19 e_1_2_8_32_1 e_1_2_8_10_1 e_1_2_8_11_1 e_1_2_8_34_1 e_1_2_8_12_1 Skafte A (e_1_2_8_47_1) 2017; 136 |
| References_xml | – volume: 25 issue: 2 year: 2018 article-title: Operational modal identification and finite element model updating of a coupled building following Bayesian approach publication-title: Struct Control Health Monit – volume: 21 start-page: 466 issue: 4 year: 2014 end-page: 483 article-title: Uncertainty analysis of system identification results obtained for a seven‐story building slice tested on the UCSD‐NEES shake table publication-title: Struct Control Health Monit – volume: 120 start-page: 96 year: 2013 end-page: 106 article-title: Experimental and computational damping estimation of an offshore wind turbine on a monopile foundation publication-title: J Wind Eng Ind Aerod – year: 2005 – volume: 74 start-page: 40 year: 2015 end-page: 45 article-title: An analytical model to predict the natural frequency of offshore wind turbines on three‐spring flexible foundations using two different beam models publication-title: Soil Dyn Earthq Eng – volume: 19 start-page: 301 issue: 2 year: 2016 end-page: 312 article-title: Variability of breaking wave characteristics and impact loads on offshore wind turbines supported by monopiles publication-title: Wind Energy – volume: 16 start-page: 637 issue: 4 year: 2002 end-page: 657 article-title: Autonomous structural health monitoring—part I: modal parameter estimation and tracking publication-title: Mech Syst Signal Proc – year: 2007 – year: 2003 – volume: 136 start-page: 261 year: 2017 end-page: 276 article-title: Experimental study of strain prediction on wave induced structures using modal decomposition and quasi static Ritz vectors publication-title: Eng Struct – volume: 7 start-page: 83 year: 2015 end-page: 96 – year: 2000 – start-page: 4693 year: 2018 end-page: 6701 – start-page: 97 year: 2017 end-page: 113 – volume: 93 start-page: 661 year: 2017 end-page: 687 article-title: Bayesian nonlinear structural FE model and seismic input identification for damage assessment of civil structures publication-title: Mech Syst Signal Proc – volume: 19 start-page: 122001‐1 issue: 12 year: 2008 end-page: 122001‐20 article-title: Structural health monitoring for a wind turbine system: a review of damage detection methods publication-title: Meas Sci Technol – volume: 8 start-page: 620 issue: 5 year: 1985 end-page: 627 article-title: An eigensystem realization algorithm for modal parameter identification and model reduction publication-title: J Guid Control Dynam – volume: 29 start-page: 310 year: 2012 end-page: 327 article-title: Joint input‐response estimation for structural systems based on reduced‐order models and vibration data from a limited number of sensors publication-title: Mech Syst Signal Proc – volume: 76 start-page: 592 year: 2016 end-page: 611 article-title: Dynamic strain estimation for fatigue assessment of an offshore monopile wind turbine using filtering and modal expansion algorithms publication-title: Mech Syst Signal Proc – year: 2014 – volume: 43 start-page: 934 issue: 5 year: 2007 end-page: 937 article-title: Unbiased minimum‐variance input and state estimation for linear discrete‐time systems with direct feedthrough publication-title: Automatica – volume: 16 start-page: 367 issue: 3 year: 2013 end-page: 381 article-title: Operational modal analysis of a 2.5 MW wind turbine using optical measurement techniques and strain gauges publication-title: Wind Energy – volume: 9 start-page: 219 year: 2017 end-page: 226 – volume: 15 start-page: 489 issue: 2 year: 2015 end-page: 503 article-title: Experimental validation of Kalman filter‐based strain estimation in structures subjected to non‐zero mean input publication-title: Smart Struct Syst – volume: 3 start-page: 349 year: 2016 end-page: 357 – volume: 18 start-page: 554 issue: 5 year: 2011 end-page: 573 article-title: Fatigue predictions in entire body of metallic structures from a limited number of vibration sensors using Kalman filtering publication-title: Struct Control Health Monit – volume: 10 start-page: 260 issue: 4 year: 1995 end-page: 277 article-title: The natural excitation technique (NExT) for modal parameter extraction from operating structures publication-title: Modal Anal Int J Anal Exp Modal Anal – volume: 68 start-page: 84 year: 2016 end-page: 104 article-title: A modal decomposition and expansion approach for prediction of dynamic responses on a monopile offshore wind turbine using a limited number of vibration sensors publication-title: Mech Syst Signal Proc – volume: 19 start-page: 1017 year: 2019 end-page: 1031 article-title: Support condition monitoring of offshore wind turbines using model updating techniques publication-title: Struct Health Monit – volume: 2 start-page: 239 year: 2019 end-page: 245 – volume: 94 start-page: 191 year: 2016 end-page: 198 article-title: Vibration‐based identification of hydrodynamic loads and system parameters for offshore wind turbine support structures publication-title: Energy Procedia – volume: 140 issue: 11 year: 2014 article-title: Aerodynamic damping and seismic response of horizontal axis wind turbine towers publication-title: J Struct Eng – year: 2006 – volume: 18 start-page: 1189 issue: 4 year: 2019 end-page: 1206 article-title: Structural health monitoring and fatigue damage estimation using vibration measurements and finite element model updating publication-title: Struct Health Monit – volume: 21 start-page: 868 issue: 6 year: 2014 end-page: 889 article-title: Simultaneous identification of structural parameters and dynamic input with incomplete output‐only measurements publication-title: Struct Control Health Monit – year: 1997 – start-page: 341 year: 2017 end-page: 364 – volume: 23 start-page: 1523 issue: 7 year: 2020 end-page: 1541 article-title: Wind turbine asymmetrical load reduction with pitch sensor fault compensation publication-title: Wind Energy – volume: 21 start-page: 1137 issue: 8 year: 2014 end-page: 1153 article-title: A multi‐reference‐based mode selection approach for the implementation of NExT–ERA in modal‐based damage detection publication-title: Struct Control Health Monit – year: 2017 – volume: 283 start-page: 1167 year: 2015 end-page: 1188 article-title: An online coupled state/input/parameter estimation approach for structural dynamics publication-title: Comput Method Appl M – year: 2015 – volume: 25 issue: 4 year: 2018 article-title: Bayesian optimal estimation for output‐only nonlinear system and damage identification of civil structures publication-title: Struct Control Health Monit – start-page: 1 year: 2014 end-page: 18 – volume: 21 start-page: 1121 issue: 11 year: 2018 end-page: 1140 article-title: Modelling damping sources in monopile‐supported offshore wind turbines publication-title: Wind Energy – ident: e_1_2_8_45_1 doi: 10.2514/3.20031 – ident: e_1_2_8_24_1 doi: 10.1002/stc.2128 – volume: 136 start-page: 261 year: 2017 ident: e_1_2_8_47_1 article-title: Experimental study of strain prediction on wave induced structures using modal decomposition and quasi static Ritz vectors publication-title: Eng Struct doi: 10.1016/j.engstruct.2017.01.014 – volume-title: Matlab—High Performance Numeric Computation and Visualization Software, User's Guide year: 2014 ident: e_1_2_8_33_1 – ident: e_1_2_8_41_1 doi: 10.1002/we.1493 – ident: e_1_2_8_46_1 doi: 10.1006/mssp.2002.1492 – ident: e_1_2_8_2_1 doi: 10.1007/978-3-319-51159-7_4 – ident: e_1_2_8_14_1 doi: 10.1016/j.automatica.2006.11.016 – ident: e_1_2_8_3_1 doi: 10.1002/we.2496 – volume-title: Statistical and Adaptive Signal Processing: Spectral Estimation, Signal Modeling, Adaptive Filtering, and Array Processing year: 2000 ident: e_1_2_8_44_1 – ident: e_1_2_8_17_1 – ident: e_1_2_8_16_1 doi: 10.1016/j.ymssp.2016.01.004 – volume: 74 start-page: 40 year: 2015 ident: e_1_2_8_36_1 article-title: An analytical model to predict the natural frequency of offshore wind turbines on three‐spring flexible foundations using two different beam models publication-title: Soil Dyn Earthq Eng doi: 10.1016/j.soildyn.2015.03.007 – volume: 19 start-page: 1017 year: 2019 ident: e_1_2_8_20_1 article-title: Support condition monitoring of offshore wind turbines using model updating techniques publication-title: Struct Health Monit doi: 10.1177/1475921719875628 – ident: e_1_2_8_32_1 doi: 10.1002/we.1833 – ident: e_1_2_8_39_1 – ident: e_1_2_8_11_1 doi: 10.1002/stc.395 – ident: e_1_2_8_23_1 doi: 10.1016/j.cma.2014.08.010 – ident: e_1_2_8_25_1 – volume: 10 start-page: 260 issue: 4 year: 1995 ident: e_1_2_8_26_1 article-title: The natural excitation technique (NExT) for modal parameter extraction from operating structures publication-title: Modal Anal Int J Anal Exp Modal Anal – ident: e_1_2_8_13_1 doi: 10.1016/j.ymssp.2012.01.011 – ident: e_1_2_8_28_1 – ident: e_1_2_8_5_1 doi: 10.1002/stc.2089 – start-page: 219 volume-title: Shock & Vibration, Aircraft/Aerospace, Energy Harvesting, Acoustics & Optics year: 2017 ident: e_1_2_8_30_1 doi: 10.1007/978-3-319-54735-0_23 – ident: e_1_2_8_42_1 doi: 10.1002/stc.1638 – ident: e_1_2_8_9_1 doi: 10.12989/sss.2015.15.2.489 – start-page: 1 volume-title: Encyclopedia of Earthquake Engineering year: 2014 ident: e_1_2_8_21_1 – ident: e_1_2_8_10_1 doi: 10.1109/EESMS.2015.7175850 – ident: e_1_2_8_43_1 doi: 10.1002/stc.1577 – volume: 19 start-page: 122001‐1 issue: 12 year: 2008 ident: e_1_2_8_4_1 article-title: Structural health monitoring for a wind turbine system: a review of damage detection methods publication-title: Meas Sci Technol doi: 10.1088/0957-0233/19/12/122001 – ident: e_1_2_8_27_1 – ident: e_1_2_8_19_1 doi: 10.1002/stc.1619 – ident: e_1_2_8_7_1 doi: 10.1016/j.ymssp.2015.07.016 – ident: e_1_2_8_48_1 doi: 10.1016/j.ymssp.2017.01.040 – ident: e_1_2_8_18_1 doi: 10.1177/1475921718790188 – volume-title: Dynamics of Structures: Theory and Applications to Earthquake Engineering year: 2017 ident: e_1_2_8_29_1 – volume: 94 start-page: 191 year: 2016 ident: e_1_2_8_22_1 article-title: Vibration‐based identification of hydrodynamic loads and system parameters for offshore wind turbine support structures publication-title: Energy Procedia doi: 10.1016/j.egypro.2016.09.222 – volume: 140 start-page: 04014090‐1‐0401 issue: 11 year: 2014 ident: e_1_2_8_40_1 article-title: Aerodynamic damping and seismic response of horizontal axis wind turbine towers publication-title: J Struct Eng doi: 10.1061/(ASCE)ST.1943-541X.0001018 – start-page: 349 volume-title: Model Validation and Uncertainty Quantification year: 2016 ident: e_1_2_8_8_1 doi: 10.1007/978-3-319-29754-5_34 – ident: e_1_2_8_35_1 – ident: e_1_2_8_37_1 doi: 10.1016/j.jweia.2013.07.004 – start-page: 239 volume-title: Dynamics of Civil Structures year: 2019 ident: e_1_2_8_6_1 doi: 10.1007/978-3-319-74421-6_32 – start-page: 341 volume-title: Risk and Reliability Analysis: Theory and Applications year: 2017 ident: e_1_2_8_31_1 – ident: e_1_2_8_38_1 doi: 10.1002/we.2218 – ident: e_1_2_8_12_1 – ident: e_1_2_8_49_1 – start-page: 83 volume-title: Structural Health Monitoring and Damage Detection year: 2015 ident: e_1_2_8_15_1 doi: 10.1007/978-3-319-15230-1_9 – ident: e_1_2_8_34_1 |
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Offshore wind turbines are complex systems operating in harsh environment. The dynamic demands in these systems often differ from values used in... Offshore wind turbines are complex systems operating in harsh environment. The dynamic demands in these systems often differ from values used in design,... |
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| SubjectTerms | Bayesian analysis Blyth wind farm Complex systems Dynamic structural analysis Fatigue failure Fatigue life Finite element method joint input‐parameter estimation Mathematical models Mechanics Mechanics (physics) Model updating Offshore offshore wind turbine Parameter estimation Parameter identification Remote monitoring Response time sequential Bayesian model updating Structural failure structural identification Turbines Twinning Wind farms Wind measurement Wind power Wind turbines |
| Title | Mechanics‐based model updating for identification and virtual sensing of an offshore wind turbine using sparse measurements |
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