Structural health monitoring (SHM) has been recognized as a useful tool for safety management and risk reduction of offshore wind farms. In complex offshore environment, jacket structures of offshore wind turbines are prone to damages due to corrosion and fatigue. Effective SHM on jacket structures can substantially reduce their operation risk and costs. This work reviews the latest progress on the SHM of offshore wind jacket structures. The achievements in the structural damage identification, location, quantification, and remaining useful life (RUL) estimation are respectively introduced in detail, and existing challenges are discussed. Possible solutions to the challenges using the Digital Twin (DT) technology are put forward. The DT is able to mirror a real jacket structure into a virtual model, and Bayesian updating can refresh the virtual model parameters in real-time to keep consistency between virtual model and physical structure; then, just-in-time SHM can be carried out for jacket structures by performing damage detection, location, quantification, and RUL estimation using the virtual model. As a result, the DT may provide engineers and researchers a practicable tool for safety monitoring and risk reduction of fixed foundation offshore wind structures.
HallowellSTMyersATArwadeSR, et al. Hurricane risk assessment of offshore wind turbines. Renew Energy2018; 125: 234–249.
2.
DeveciMÖzcanEJohnR, et al. A study on offshore wind farm siting criteria using a novel interval-valued fuzzy-rough based Delphi method. J Environ Manag2020; 270: 110916.
3.
CausonPDJudeSGillAB, et al. Critical evaluation of ecosystem changes from an offshore wind farm: producing natural capital asset and risk registers. Environ Sci Policy2022; 136: 772–785.
4.
MehrjooASongMMoaveniB, et al. Optimal sensor placement for parameter estimation and virtual sensing of strains on an offshore wind turbine considering sensor installation cost. Mech Syst Signal Process2022; 169: 108787.
5.
ZhangXSunLSunH, et al. Floating offshore wind turbine reliability analysis based on system grading and dynamic FTA. J Wind Eng Ind Aerodynamics2016; 154: 21–33.
6.
AlbiezMDammJUmmenhoferT, et al. Hybrid joining of jacket structures for offshore wind turbines – validation under static and dynamic loading at medium and large scale. Eng Struct2022; 252: 113595.
7.
Moghaddam NiaHAghakouchakAA. Evaluating frequency domain methods for fatigue analysis of fixed jacket type offshore platform. Ocean Eng2022; 246: 110233.
8.
TaoFZhangHLiuA, et al. Digital Twin in industry: State-of-the-Art. IEEE Trans Ind Inform2019; 15(4): 2405–2415.
9.
GuoYWangHLianJ.Review of integrated installation technologies for offshore wind turbines: current progress and future development trends. Energy Convers Manag2022; 255: 115319.
10.
RenZVermaASLiY, et al. Offshore wind turbine operations and maintenance: a state-of-the-art review. Renew Sustain Energ Rev2021; 144: 110886.
11.
JamesMHaldarS.Seismic vulnerability of jacket supported large offshore wind turbine considering multidirectional ground motions. Structures2022; 43: 407–423.
12.
NatarajanKMadabhushiGSP. Seismic response of an offshore wind turbine jacket structure with pile foundations. Soil Dyn Earthq Eng2022; 162: 107427.
13.
MendozaJNielsenJSSørensenJD, et al. Structural reliability analysis of offshore jackets for system-level fatigue design. Struct Saf2022; 97: 102220.
14.
CrossE.2012. On structural health monitoring in changing environmental and operational conditions. PhD thesis, University of Sheffield, Sheffield.
15.
FarrarCWordenK.Structural health monitoring: a machine learning perspective. Chichester: John Wiley & Sons, 2012.
16.
MalekzehtabHGolafshaniAA.Damage Detection in an offshore jacket platform using genetic algorithm based finite element model updating with noisy modal data. Procedia Eng2013; 54: 480–490.
17.
MitchellDBlancheJHarperS, et al. A review: challenges and opportunities for artificial intelligence and robotics in the offshore wind sector. Energy and AI2022; 8: 100146.
18.
LekE.Digital twin strategy improves offshore oil and gas production. Offshore2019; 79(4): 46–48.
19.
AeranASiriwardaneSCMikkelsenO, et al. A framework to assess structural integrity of ageing offshore jacket structures for life extension. Mar Struct2017; 56: 237–259.
20.
VidalYAquinoGPozoF, et al. Structural health monitoring for jacket-type offshore wind turbines: experimental proof of concept. Sensors2020; 20: 1835.
21.
RosyadiM, et al. Stabilization of wind farms by PMSG based variable speed wind generator with adaptive Pi-Fuzzy controller. J Power Energy Eng2011; 34: 1–6.
22.
VieiraMHenriquesESnyderB, et al. Insights on the impact of structural health monitoring systems on the operation and maintenance of offshore wind support structures. Struct Saf2022; 94: 102154.
23.
GeorgeBLooJJieW.Recent advances and future trends on maintenance strategies and optimisation solution techniques for offshore sector. Ocean Eng2022; 250: 110986.
24.
SalawuOS.Detection of structural damage through changes in frequency: a review. Eng Struct1997; 19(9): 718–723.
ZhangYMiyamoriYMikamiS, et al. Vibration-based structural state identification by a 1-dimensional convolutional neural network. Comput Aided Civ Infrastruct Eng2019; 34(9): 822–839.
27.
LiuFLiHLiW, et al. Lower-order modal parameters identification for offshore jacket platform using reconstructed responses to a sea test. Appl Ocean Res2014; 46: 124–130.
28.
LiuKYanRJGuedes SoaresC.Damage identification in offshore jacket structures based on modal flexibility. Ocean Eng2018; 170: 171–185.
29.
RichmondMSmolkaUKoliosA.Feasibility for damage identification in offshore wind jacket structures through monitoring of global structural dynamics. Energies2020; 13(21): 5791.
30.
PooyaSMHMassumiA. A novel damage detection method in beam-like structures based on the relation between modal kinetic energy and modal strain energy and using only damaged structure data. J Sound Vib2022; 530: 116943.
31.
GreśSRivaRSüleymanCY, et al. Uncertainty quantification of modal parameter estimates obtained from subspace identification: an experimental validation on a laboratory test of a large-scale wind turbine blade. Eng Struct2022; 256: 114001.
32.
EwaldVSridaran VenkatRAsokkumarA, et al. Perception modelling by invariant representation of deep learning for automated structural diagnostic in aircraft maintenance: a study case using DeepSHM. Mech Syst Signal Process2022; 165: 108153.
33.
YenBHiokaYSchmidG, et al. Multi-sensory sound source enhancement for unmanned aerial vehicle recordings. Appl Acoust2022; 189: 108590.
34.
ElshafeyAAHaddaraMRMarzoukH.Damage detection in offshore structures using neural networks. Mar Struct2010; 23(1): 131–145.
35.
MojtahediALotfollahi YaghinMAHassanzadehY, et al. A robust damage detection method developed for offshore jacket platforms using modified artificial immune system algorithm. China Ocean Eng2012; 26: 379–395.
36.
MojtahediAHokmabadyHAbyanehSSZ, et al. Establishment of a hybrid Fuzzy–Krill Herd approach for novelty detection applied to damage classification of offshore jacket-type structures. J Mar Sci Technol2019; 24(3): 812–829.
37.
EreizSDuvnjakIFernando Jiménez-AlonsoJ.Review of finite element model updating methods for structural applications. Structures2022; 41: 684–723.
38.
Lotfollahi-YaghinMA, et al. 2011. Structural Health Monitoring (SHM) of offshore jacket platforms. In: Proceedings of the ASME 2011 30th international conference on ocean, offshore and arctic engineering. Volume 1: Offshore Technology; Polar and Arctic Sciences and Technology, Rotterdam, The Netherlands, 19–24 June 2011, pp. 579–588. ASME.
39.
DiaoY, et al. Structural damage identification of offshore jacket platform based on the pseudofree response data. In: ASME 2014 33rd international conference on ocean, offshore and arctic engineering, San Francisco, CA, 8–13 June 2014.
40.
YiJ-H.Laboratory tests on local damage detection for jacket-type offshore structures using optical FBG sensors based on statistical approaches. Ocean Eng2016; 124: 94–103.
41.
MojtahediAHokmabadyHYaghubzadehA, et al. An improved model reduction-modal based method for model updating and health monitoring of an offshore jacket-type platform. Ocean Eng2020; 209: 107495.
42.
HuangYHuangS.Neural network-based prediction of topside mass of an in-service jacket platform. Ocean Eng2022; 246: 110554.
43.
MironovsDRučevskisSDzelzītisK.Prospects of structural damage identification using modal analysis and anomaly detection. Procedia Struct Integr2022; 37: 410–416.
44.
TiturusBFriswellMIStarekL.Damage detection using generic elements: Part II. Damage detection. Comput Struct2003; 81(24–25): 2287–2299.
45.
YangH-Z, et al. Damage localization for offshore structures by modal strain energy decomposition method. Revista Espanola De Cardiologia – Rev Espan Cardiol2004; 5: 4207–4212.
46.
WangS.Damage detection in offshore platform structures from limited modal data. Appl Ocean Res2013; 41: 48–56.
47.
HaeriMHLotfiADolatshahiKM, et al. Inverse vibration technique for structural health monitoring of offshore jacket platforms. Appl Ocean Res2017; 62: 181–198.
48.
YanYYangYBashirM, et al. Dynamic analysis of 10 MW offshore wind turbines with different support structures subjected to earthquake loadings. Renew Energy2022; 193: 758–777.
49.
XuMGuoJWangS, et al. Structural damage identification with limited modal measurements and ultra-sparse Bayesian regression. Struct Control Health Monit2021; 28: e2729.
50.
XuMWangSJiangY.Structural damage identification by a cross modal energy sensitivity based mode subset selection strategy. Mar Struct2021; 77: 102968.
51.
LiuFGaoSHanH, et al. Interference reduction of high-energy noise for modal parameter identification of offshore wind turbines based on iterative signal extraction. Ocean Eng2019; 183: 372–383.
52.
GordanMSabbagh-YazdiSRIsmailZ, et al. State-of-the-art review on advancements of data mining in structural health monitoring. Measurement2022; 193: 110939.
53.
MangalLIdichandyVGGanapathyC.Structural monitoring of offshore platforms using impulse and relaxation response. Ocean Eng2001; 28(6): 689–705.
54.
AsgarianBAghaeidoostVShokrgozarHR.Damage detection of jacket type offshore platforms using rate of signal energy using wavelet packet transform. Mar Struct2016; 45: 1–21.
55.
GeristSMaheriMR.Structural damage detection using imperialist competitive algorithm and damage function. Appl Soft Comput2019; 77: 1–23.
56.
BaoXWangZIglesiasG.Damage detection for offshore structures using long and short-term memory networks and random decrement technique. Ocean Eng2021; 235: 109388.
57.
LarsenMLBaumgartnerJClausenHB, et al. A new constant amplitude equivalent principal component analysis-based method for non-proportionality quantification of variable amplitude loaded welded joints in large-scale structures. Int J Fatigue2022; 163: 107008.
58.
MinSJeongKNohY, et al. Damage detection for tethers of submerged floating tunnels based on convolutional neural networks. Ocean Eng2022; 250: 111048.
59.
LiJLiuTWangX, et al. Automated asphalt pavement damage rate detection based on optimized GA-CNN. Autom Constr2022; 136: 104180.
60.
DaneshvarMHSarmadiH.Unsupervised learning-based damage assessment of full-scale civil structures under long-term and short-term monitoring. Eng Struct2022; 256: 114059.
61.
DaiL, et al. Cross modal adaptive Few-Shot learning based on task dependence. Chin J Electron2022; 32: 1–12.
62.
HwangHYKimC.Damage detection in structures using a few frequency response measurements. J Sound Vib2004; 270(1–2): 1–14.
63.
PérezJERRodríguezRVázquez-HernándezAO.Damage detection in offshore jacket platforms with limited modal information using the damage submatrices method. Mar Struct2017; 55: 78–103.
64.
KimDHLeeSG.Reliability analysis of offshore wind turbine support structures under extreme ocean environmental loads. Renew Energy2015; 79: 161–166.
65.
Guedes SoaresCGarbatovY. Fatigue reliability assessment of an offshore wind turbine supporting structure accounting for inspection and Repair. Boca Raton, FL: CRC Press, 2015. pp.2015.
66.
ZhuSPKeshtegarBBen SeghierMEA, et al. Hybrid and enhanced PSO: Novel first order reliability method-based hybrid intelligent approaches. Comput Methods Appl Mech Eng2022; 393: 114730.
67.
YaoJWuWLiS.Anomaly detection model of mooring system based on LSTM PCA method. Ocean Eng2022; 254: 111350.
68.
HuS-LJLiHWangS.Cross-model cross-mode method for model updating. Mech Syst Signal Process2007; 21(4): 1690–1703.
69.
LiHWangJJames HuSL. Using incomplete modal data for damage detection in offshore jacket structures. Ocean Eng2008; 35(17–18): 1793–1799.
70.
HosseinlouFMojtahediALotfollahi YaghinMA.Developing a SIM strategy for offshore jacket platforms based on the FE model updating and a novel simplified method. Ocean Eng2017; 145: 158–176.
71.
ZargarzadehAMojtahediAMohammadyzadehS, et al. Employing an improved cross model cross mode algorithm for damage detection of a steel offshore platform frame using experimental data. Structures2020; 28: 1589–1600.
72.
DoeblingSWFarrarCRPrimeMB. A summary review of vibration-based damage identification methods. Shock Vibr Dig1998; 30: 91–105.
73.
MaiQ, et al. 2016. Updating failure probability of a welded joint in offshore wind turbine substructures. In: The 35th international conference on ocean, offshore and arctic engineering conference (OMAE16), Busan, South Korea.
74.
DongWMoanTGaoZ.Long-term fatigue analysis of multi-planar tubular joints for jacket-type offshore wind turbine in time domain. Eng Struct2011; 33: 2002–2014.
75.
DongWMoanTGaoZ.Fatigue reliability analysis of the jacket support structure for offshore wind turbine considering the effect of corrosion and inspection. Reliab Eng Syst Saf2012; 106: 11–27.
76.
YeterBGarbatovYGuedes SoaresC.Evaluation of fatigue damage model predictions for fixed offshore wind turbine support structures. Int J Fatigue2016; 87: 71–80.
77.
AeranAAcostaRSiriwardaneSC, et al. A nonlinear fatigue damage model: comparison with experimental damage evolution of S355 (SAE 1020) structural steel and application to offshore jacket structures. Int J Fatigue2020; 135: 105568.
78.
ShittuAAMehmanparastAHartP, et al. Comparative study between S-N and fracture mechanics approach on reliability assessment of offshore wind turbine jacket foundations. Reliab Eng Syst Saf2021; 215: 107838.
79.
HanCLiuKMaY, et al. Multiaxial fatigue assessment of Jacket-supported offshore wind turbines considering multiple random correlated loads. Renew Energy2021; 169: 1252–1264.
80.
MohammadiSFGalgoulNSStarossekU, et al. An efficient time domain fatigue analysis and its comparison to spectral fatigue assessment for an offshore jacket structure. Mar Struct2016; 49: 97–115.
81.
JuSHSuFCKeYP, et al. Fatigue design of offshore wind turbine jacket-type structures using a parallel scheme. Renew Energy2019; 136: 69–78.
MesmacqueGGarciaSAmroucheA, et al. Sequential law in multiaxial fatigue, a new damage indicator. Int J Fatigue2005; 27(4): 461–467.
84.
SureshS.Fatigue of materials: Fatigue crack growth in brittle solids. 2nd ed.Cambridge, UK: Cambridge University Press, 1998.
85.
WuYWWuHTChungCH, et al. Fatigue calculation of offshore jacket structural for monitoring. IOP Conf Ser Mater Sci Eng2019; 588: 012015.
86.
WangMWangCHnydiuk-StefanA, et al. Recent progress on reliability analysis of offshore wind turbine support structures considering digital twin solutions. Ocean Eng2021; 232: 109168.
87.
ShittuAAMehmanparastAShafieeM, et al. Structural reliability assessment of offshore wind turbine support structures subjected to pitting corrosion-fatigue: a damage tolerance modelling approach. Wind Energy2020; 23: 2004–2026.
88.
VijayanSRamRSVelanDS.Dynamic spectral fatigue behavior of a fixed offshore jacket structure. Mater Today Proc2021; 46: 3533–3537.
89.
LarsenMLAroraVLützenM, et al. Fatigue life estimation of the weld joint in K-node of the offshore jacket structure using stochastic finite element analysis. Mar Struct2021; 78: 103020.
90.
HokmabadyHMohammadyzadehSMojtahediA.Suppressing structural vibration of a jacket-type platform employing a novel magneto-rheological tuned liquid column gas damper (MR-TLCGD). Ocean Eng2019; 180: 60–70.
91.
HokmabadyHMojtahediAMohammadyzadehS.Uncertainty analysis of an offshore jacket-type platform using a developed numerical model updating technique. Ocean Eng2020; 211: 107608.
92.
MartinSMWälchliDArampatzisG, et al. Korali: Efficient and scalable software framework for Bayesian uncertainty quantification and stochastic optimization. Comput Methods Appl Mech Eng2022; 389: 114264.
93.
WangMFengSIncecikA, et al. Structural fatigue life prediction considering model uncertainties through a novel digital twin-driven approach. Comput Methods Appl Mech Eng2022; 391: 114512.
94.
JeffreysH.Small corrections in the theory of Surface Waves. Geophys J Int1961; 6: 115–117.
95.
CoxR.Algebra of probable inference. Baltimore, Maryland: Johns Hopkins University Press, 2001. pp.9780801869822.
96.
JaynesETJ. Prior probabilities. Systems science and Cybernetics. IEEE Trans1968; 4: 227–241.
97.
CubillosPEHarringtonJBlecicJ, et al. An open-source Bayesian atmospheric radiative transfer (BART) code. II. The transit radiative transfer module and retrieval of HAT-P-11b. Planet Sci J2022; 3: 1–16.
98.
XieXLuQParlikadAK, et al. Digital twin enabled asset anomaly detection for building facility management. IFAC-PapersOnLine2020; 53: 380–385.
99.
LavesMHTölleMSchlaeferA, et al. Posterior temperature optimized Bayesian models for inverse problems in medical imaging. Med Image Anal2022; 78: 102382.
100.
XiongHLiuSSharanRV, et al. Weak label based Bayesian U-net for optic disc segmentation in fundus images. Artif Intell Med2022; 126: 102261.
101.
PedersenCLRitterT.Updating the theory of industrial marketing: Industrial marketing as a Bayesian process of belief-updating. Ind Mark Manag2022; 102: 403–420.
102.
LuanSKeRHuangZ, et al. Traffic congestion propagation inference using dynamic Bayesian graph convolution network. Transp Res Part C Emerg Technol2022; 135: 103526.
103.
MaiQAWeijtjensWDevriendtC, et al. Prediction of remaining fatigue life of welded joints in wind turbine support structures considering strain measurement and a joint distribution of oceanographic data. Mar Struct2019; 66: 307–322.
104.
FathiAEsfandiariAFadavieM, et al. Damage detection in an offshore platform using incomplete noisy FRF data by a novel Bayesian model updating method. Ocean Eng2020; 217: 108023.
105.
Cantero-ChinchillaSMalikMKChronopoulosD, et al. Bayesian damage localization and identification based on a transient wave propagation model for composite beam structures. Compos Struct2021; 267: 113849.
106.
CordeiroCEStutzLTKnuppDC, et al. Generalized integral transform and Hamiltonian Monte Carlo for Bayesian structural damage identification. Appl Math Model2022; 104: 243–258.
107.
ZhangLSuoWZhangZ, et al. Extending Bayesian learning to intelligent learning in human way. Procedia Comput Sci2022; 199: 118–124.
108.
KwonD-SJinCKimM.Prediction of dynamic and structural responses of submerged floating tunnel using artificial neural network and minimum sensors. Ocean Eng2022; 244: 110402.
109.
SotoudehniaEShahabianFAftabi SaniA.Damage detection of cylindrical shells based on Sander's theory and model updating using incomplete modal data considering random noises. Eur J Mech A Solids2021; 85: 104110.
110.
BalakrishnanS, et al. The prognostics of Digital Twin Technology for Industry 4.0. CSI Communications2020; 44(6): 33–35.
111.
StavropoulosPMourtzisD.Digital twins in industry 4.0. Amsterdam, The Netherlands: Elsevier, 2022, pp.277–316.
112.
GanguliRAdhikariS.The digital twin of discrete dynamic systems: Initial approaches and future challenges. Appl Math Model2020; 77: 1110–1128.
113.
JonesDSniderCNassehiA, et al. Characterising the Digital Twin: a systematic literature review. CIRP J Manuf Sci Technol2020; 29: 36–52.
114.
WaggDJWordenKBarthorpeRJ, et al. Digital Twins: State-of-The-Art and future directions for modeling and simulation in Engineering Dynamics Applications. ASCE-ASME J Risk Uncert Eng Syst Part B Mech Eng2020; 6: 030901.
115.
TuegelEJIngraffeaAREasonTG, et al. Reengineering aircraft structural life prediction using a digital twin. Int J Aerosp Eng2011; 2011: 1–14.
116.
GrievesMVickersJ.Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems. In: KahlenF-JFlumerfeltSAlvesA (eds) Transdisciplinary perspectives on complex systems. Cham: Springer, 2017, pp. 85–113.
117.
NegriEFumagalliLMacchiM.A review of the roles of Digital Twin in CPS-based production systems. Procedia Manuf2017; 11: 939–948.
118.
HaagSAnderlR.Digital Twin – proof of concept. Manuf Lett2018; 15: 64–66.
119.
AutiosaloJVepsalainenJViitalaR, et al. A feature-based framework for Structuring Industrial Digital Twins. IEEE Access2020; 8: 1193–1208.
120.
GlaessgenEStargelD.The digital twin paradigm for future NASA and U.S. Air Force Vehicles. 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference2012. Honolulu, Hawaii, USA, 23–26 April, 2012, vol. 53, p 1818.
121.
TaoFChengJQiQ, et al. Digital twin-driven product design, manufacturing and service with big data. Int J Adv Manuf Technol2018; 94: 3563–3576.
122.
TaoFZhangMLiuY, et al. Digital twin driven prognostics and health management for complex equipment. Cirp Annals2018; 67: 169–172.
123.
GrievesM.Product lifecycle management – driving the next generation of Lean Thinking. New York: McGraw-Hill, 2006. pp.2006.
124.
FangXWangHLiW, et al. Fatigue crack growth prediction method for offshore platform based on digital twin. Ocean Eng2022; 244: 110320.
125.
PitchaikaniA, et al. 2019. Liquid cooling applications in twin builder-industrial paper. In: The 2nd Japanese Modelica conference, Tokyo, Japan, pp. 49–52.
126.
KalantariSPourjabarSXuTB, et al. Developing and user-testing a “Digital Twins” prototyping tool for architectural design. Autom Constr2022; 135: 104140.
127.
LiuCLe RouxLKörnerC, et al. Digital Twin-enabled collaborative data management for metal additive manufacturing systems. J Manuf Syst2022; 62: 857–874.
128.
OpokuDGJPereraSOsei-KyeiR, et al. Digital twin application in the construction industry: a literature review. J Build Eng2021; 40: 102726.
129.
SemeraroCLezocheMPanettoH, et al. Digital twin paradigm: a systematic literature review. Comput Ind2021; 130: 103469.
130.
GargSGogoiAChakrabortyS, et al. Machine learning based digital twin for stochastic nonlinear multi-degree of freedom dynamical system. Probab Eng Mech2021; 66: 103173.
131.
GhoshAKUllahASTetiR, et al. Developing sensor signal-based digital twins for intelligent machine tools. J Ind Inf Integr2021; 24: 100242.
132.
VoosenP.Europe builds ‘digital twin’ of Earth to hone climate forecasts. Science2020; 370: 16–17.
133.
HunheviczJJMotieMHallDM.Digital building twins and blockchain for performance-based (smart) contracts. Autom Constr2022; 133: 103981.
134.
Rachana HarishALiuXLZhongRY, et al. Log-flock: a blockchain-enabled platform for digital asset valuation and risk assessment in E-commerce logistics financing. Comput Ind Eng2021; 151: 107001.
135.
TariqRTorres-AguilarCESheikhNA, et al. Data engineering for digital twining and optimization of naturally ventilated solar façade with phase changing material under global projection scenarios. Renew Energy2022; 187: 1184–1203.
136.
QiQTaoFHuT, et al. Enabling technologies and tools for digital twin. J Manuf Syst2021; 58: 3–21.
137.
AkintsevaAVPereverzevPPReshetnikovBV, et al. Analytical basics of digital twin for CNC round grinding process. Mater Today Proc2021; 38: 1740–1744.
138.
RuppertTAbonyiJ.Integration of real-time locating systems into digital twins. J Ind Inf Integr2020; 20: 100174.
139.
MiSFengYZhengH, et al. Prediction maintenance integrated decision-making approach supported by digital twin-driven cooperative awareness and interconnection framework. J Manuf Syst2021; 58: 329–345.
140.
AksenovVSemochkinABendikA, et al. Utilizing digital twin for maintaining safe working environment among Railway Track Tamping Brigade. Transp Res Procedia2022; 61: 600–608.
141.
ZhouCXuJMiller-HooksE, et al. Analytics with digital-twinning: a decision support system for maintaining a resilient port. Decis Support Syst2021; 143: 113496.
142.
JiangFDingYSongY, et al. Digital Twin-driven framework for fatigue life prediction of steel bridges using a probabilistic multiscale model: application to segmental orthotropic steel deck specimen. Eng Struct2021; 241: 112461.
143.
GhorbaniHKhameneifarF.Construction of damage-free digital twin of damaged aero-engine blades for repair volume generation in remanufacturing. Robot Comput Integr Manuf2022; 77: 102335.
144.
GiannarosEKotzakoliosAKostopoulosV, et al. Low- and high-fidelity modeling of sandwich-structured composite response to bird strike, as tools for a digital-twin-assisted damage diagnosis. Int J Impact Eng2022; 160: 104058.
145.
LeserPEWarnerJELeserWP, et al. A digital twin feasibility study (Part II): Non-deterministic predictions of fatigue life using in-situ diagnostics and prognostics. Eng Fract Mech2020; 229: 106903.
146.
VanDerHornEWangZMahadevanS.Towards a digital twin approach for vessel-specific fatigue damage monitoring and prognosis. Reliab Eng Syst Saf2022; 219: 108222.
147.
ZhangMTaoFHuangB, et al. Digital twin data: methods and key technologies. Digital Twin2021; 1: 2.
148.
DongYTanRZhangP, et al. Product redesign using functional backtrack with digital twin. Adv Eng Inform2021; 49: 101361.
149.
TaoFXiaoBQiQ, et al. Digital twin modeling. J Manuf Syst2022; 64: 372–389.
