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https://doi.org/10.2139/ssrn.4667824

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/stc.395openalex-extracted
Fatigue predictions in entire body of metallic structures from a limited number of vibration sensors using Kalman filtering
inferredcites →https://doi.org/10.1002/we.2136openalex-extracted
Measurement of mechanical loads in large wind turbines: Problems on calibration of strain gage bridges and analysis of uncertainty
inferredcites →https://doi.org/10.1002/we.2762openalex-extracted
Virtual sensors for wind turbines with machine learning‐based time series models
inferredcites →https://doi.org/10.1007/978-0-387-92897-5_1076openalex-extracted
Fatigue Cycle Counting
inferredcites →https://doi.org/10.1007/978-1-4020-6808-9_1openalex-extracted
Introduction to Fatigue of Structures and Materials
inferredcites →https://doi.org/10.1007/978-3-030-81716-9_17openalex-extracted
Physics-Informed Machine Learning for Structural Health Monitoring
inferredcites →https://doi.org/10.1007/978-3-031-07322-9_48openalex-extracted
Integrating Physical Knowledge into Gaussian Process Regression Models for Probabilistic Fatigue Assessment
inferredcites →https://doi.org/10.1007/s12599-021-00689-wopenalex-extracted
Virtual Sensors
inferredcites →https://doi.org/10.1016/j.automatica.2006.08.002openalex-extracted
Unbiased minimum-variance input and state estimation for linear discrete-time systems
inferredcites →https://doi.org/10.1016/j.marstruc.2021.103019openalex-extracted
Feasibility of modal expansion for virtual sensing in offshore wind jacket substructures
inferredcites →https://doi.org/10.1016/j.renene.2021.02.003openalex-extracted
Virtual fatigue diagnostics of wake-affected wind turbine via Gaussian Process Regression
inferredcites →https://doi.org/10.1016/j.renene.2023.01.093openalex-extracted
Long-term fatigue estimation on offshore wind turbines interface loads through loss function physics-guided learning of neural networks
inferredcites →https://doi.org/10.1016/j.renene.2023.119430openalex-extracted
System identification and finite element model updating of a 6 MW offshore wind turbine using vibrational response measurements
inferredcites →https://doi.org/10.1016/j.ymssp.2012.01.011openalex-extracted
Joint input-response estimation for structural systems based on reduced-order models and vibration data from a limited number of sensors
inferredcites →https://doi.org/10.1016/j.ymssp.2015.07.016openalex-extracted
A modal decomposition and expansion approach for prediction of dynamic responses on a monopile offshore wind turbine using a limited number of vibration sensors
inferredcites →https://doi.org/10.1016/j.ymssp.2016.01.004openalex-extracted
Dynamic strain estimation for fatigue assessment of an offshore monopile wind turbine using filtering and modal expansion algorithms
inferredcites →https://doi.org/10.1016/j.ymssp.2021.108787openalex-extracted
Optimal sensor placement for parameter estimation and virtual sensing of strains on an offshore wind turbine considering sensor installation cost
inferredcites →https://doi.org/10.1016/j.ymssp.2023.110425openalex-extracted
Joint parameter-input estimation for digital twinning of the Block Island wind turbine using output-only measurements
inferredcites →https://doi.org/10.1016/j.ymssp.2023.110488openalex-extracted
Virtual sensing of subsoil strain response in monopile-based offshore wind turbines via Gaussian process latent force models
inferredcites →https://doi.org/10.1017/dce.2022.38openalex-extracted
Virtual sensing in an onshore wind turbine tower using a Gaussian process latent force model
inferredcites →https://doi.org/10.1088/1742-6596/2265/3/032080openalex-extracted
Reconstructing the bending moments time history of wind turbine tower from acceleration measurements using Gaussian processes
inferredcites →https://doi.org/10.1109/icnsc.2009.4919241openalex-extracted
Virtual sensing techniques and their applications
inferredcites →https://doi.org/10.1109/jstsp.2016.2570738openalex-extracted
Gaussian Process Regression Stochastic Volatility Model for Financial Time Series
inferredcites →https://doi.org/10.1109/tie.2015.2442212openalex-extracted
A Performance Monitoring Approach for the Novel Lillgrund Offshore Wind Farm
inferredcites →https://doi.org/10.1115/omae2023-101045openalex-extracted
Uncertainty of Virtually Sensed Stress Ranges in Offshore Wind Support Structures
inferredcites →https://doi.org/10.1142/s0129065704001899openalex-extracted
GAUSSIAN PROCESSES FOR MACHINE LEARNING
inferredcites →https://doi.org/10.3929/ethz-b-000346296openalex-extracted
Fatigue stress estimation of offshore wind turbine using a Kalman filter in combination with accelerometers
inferredcites →https://doi.org/10.48550/arxiv.2009.10862openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.2207.05901openalex-extracted
inferredcites →https://doi.org/10.7551/mitpress/3206.001.0001openalex-extracted
Gaussian Processes for Machine Learning

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1201/9781003595120-92openalex-snapshot
Sustainable digital twins for deteriorating offshore wind turbine structures
inferred← citeshttps://doi.org/10.2139/ssrn.5218053openalex-snapshot
Self-Adaptive Nonlinear Discrete Grey Model Based on Priority-Varying Weighted Accumulation for Forecasting Wind Power Generation Under New Information Uncertainty
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