Wind turbine fatigue estimation is based on time\uffe2\uff80\uff90consuming Monte Carlo simulations for various wind conditions, followed by cycle\uffe2\uff80\uff90counting procedures and the application of engineering damage models. The outputs of the fatigue simulations are large in volume and of high dimensionality, as they typically consist of estimates on finite\uffe2\uff80\uff90element computational meshes. The strain and stress tensor time series, which are the primary quantities of interest when considering the problem of fatigue estimation, are dictated by complex vibration characteristics due to the coupled effect of aerodynamics, structural dynamics, geometrically non\uffe2\uff80\uff90linear mechanics, and control. A Variational Auto\uffe2\uff80\uff90Encoder (VAE) is trained in order to model the probability distribution of the accumulated fatigue on the root cross\uffe2\uff80\uff90section of a simulated wind turbine blade. The VAE is conditioned on historical data that correspond to coarse wind\uffe2\uff80\uff90field measurement statistics, such as mean hub\uffe2\uff80\uff90height wind speed, standard deviation of hub\uffe2\uff80\uff90height wind speed and shear exponent. In the absence of direct measurements of structural loads, the proposed technique finds applications in making long\uffe2\uff80\uff90term probabilistic deterioration predictions from historical Supervisory, Control, and Data Acquisition (SCADA) data, while capturing the inherent aleatoric uncertainty due to the incomplete information on strain time series of the wind turbine structure, when only SCADA data statistics are available.
", "keywords": ["CVAE", "deep generative models", "high dimensional simulation outputs", "uncertainty quantification", "TJ807-830", "blade root fatigue", "conditional variational autoencoder", "SCADA", "wind turbine blade", "7. Clean energy", "Renewable energy sources"]}, "links": [{"href": "https://doi.org/10.1002/we.2621"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Wind%20Energy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1002/we.2621", "name": "item", "description": "10.1002/we.2621", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/we.2621"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-02-11T00:00:00Z"}}, {"id": "10.1016/j.proeng.2017.09.285", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-22T16:16:18Z", "type": "Journal Article", "created": "2017-09-12", "title": "A substructure approach for fatigue assessment on wind turbine support structures using output-only measurements", "description": "Open AccessFatigue constitutes a major and highly-uncertain safety-related factor for wind turbines. In order to ensure a reliable fatigue assessment of such structures, it is essential that stress predictions be based on the actual structural behaviour. The response identification of operational wind turbines in a global framework constitutes a challenging problem due to the uncertainties associated with the variability of the wind loading and the dynamics of the rotor. In reducing these uncertainties, this study proposes a substructuring approach, which abolishes the need for modelling the intricate and time-varying dynamics of the rotor. Instead, response prediction is performed on a substructure model of the tower and the effect of wind loads and servo dynamics is accounted for via the estimated interface forces at the top of the support structure. The application is based on synthetic vibration data generated via the FAST software and an output-only Bayesian filter employing the structural model of the support structure. The effectiveness of the proposed framework is presented in terms of fatigue damage estimates at different locations on the tower.", "keywords": ["Wind turbine; Dynamic substructuring; Input-state estimation; Response identification; Fatigue damage", "Response identification", "Input-state estimation", "Dynamic substructuring", "Fatigue damage", "02 engineering and technology", "Wind turbine", "7. Clean energy", "0201 civil engineering"]}, "links": [{"href": "https://doi.org/10.1016/j.proeng.2017.09.285"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Procedia%20Engineering", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.proeng.2017.09.285", "name": "item", "description": "10.1016/j.proeng.2017.09.285", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.proeng.2017.09.285"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-01-01T00:00:00Z"}}, {"id": "10.1016/j.proeng.2017.09.509", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-22T16:16:18Z", "type": "Journal Article", "created": "2017-09-12", "title": "Fatigue assessment of a wind turbine blade when output from multiple aero-elastic simulators are available", "description": "Open AccessAero-elasticity is a term that refers to the interaction between the aerodynamic, inertial and elastic loads when a structure is exposed to fluid flow such as turbulent wind inflow. Various commercial and research-based simulators are available to compute the wind turbine aero-elastic loads. These aero-elastic simulators are of varying complexity and might bear different underlying assumptions, pertaining to physics, mathematical and computational formulations. However, currently established practice dictates that the adopted aero-elastic simulators are verified and validated on the basis of measurements from test wind turbines. As a result, it is generally hard to establish one simulator as superior to another in terms of their predicted output. The objective in this paper is to statistically aggregate the fatigue load on a wind turbine blade when simultaneous simulations are performed using multiple simulators. The simulators of the wind turbine blade are of varying fidelity, and uncertainty in the modelling and assumptions on the model inputs are implicitly included, and taken into account in the statistical analysis. The main concept followed here is that rather than treating the output of the simulators as individual information sources, we consider them as part of an ensemble, which can be clustered and then aggregated to predict the \u201cmost likely\u201d fatigue load, hence reducing the inherent model-form uncertainty.", "keywords": ["Finite elements", "Uncertainty", "Wind turbine; Aeroelasticity; Uncertainty; Fatigue; Ensemble Aggregation; Data fusion; Finite elements; Machine learning", "02 engineering and technology", "Data fusion", "7. Clean energy", "01 natural sciences", "0201 civil engineering", "Ensemble Aggregation", "Machine learning", "Aeroelasticity", "0101 mathematics", "Wind turbine", "Fatigue"]}, "links": [{"href": "https://doi.org/10.1016/j.proeng.2017.09.509"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Procedia%20Engineering", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.proeng.2017.09.509", "name": "item", "description": "10.1016/j.proeng.2017.09.509", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.proeng.2017.09.509"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-01-01T00:00:00Z"}}, {"id": "10.1016/j.renene.2021.02.003", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-22T16:16:19Z", "type": "Journal Article", "created": "2020-11-05", "title": "Virtual fatigue diagnostics of wake-affected wind turbine via Gaussian Process Regression", "description": "We propose a data-driven model to predict the short-term fatigue Damage Equivalent Loads (DEL) on a wake-affected wind turbine based on wind field inflow sensors and/or loads sensors deployed on an adjacent up-wind wind turbine. Gaussian Process Regression (GPR) with Bayesian hyperparameters calibration is proposed to obtain a surrogate from input random variables to output DELs in the blades and towers of the up-wind and wake-affected wind turbines. A sensitivity analysis based on the hyperparameters of the GPR and Kullback-Leibler divergence is conducted to assess the effect of different input on the obtained DELs. We provide qualitative recommendations for a minimal set of necessary and sufficient input random variables to minimize the error in the DEL predictions on the wake-affected wind turbine. Extensive simulations are performed comprising different random variables, including wind speed, turbulence intensity, shear exponent and inflow horizontal skewness. Furthermore, we include random variables related to the blades lift and drag coefficients with direct impact on the rotor aerodynamic induction, which governs the evolution and transport of the meandering wake. In addition, different spacing between the wind turbines and W\u00f6hler exponents for calculation of DELs are considered. The maximum prediction normalized mean squared error, obtained in the tower base DELs in the fore-aft direction of the wake affected wind turbine, is less than 4%. In the case of the blade root DELs, the overall prediction error is less than 1%. The proposed scheme promotes utilization of sparse structural monitoring (loads) measurements for improving diagnostics on wake-affected turbines.
", "keywords": ["bepress|Physical Sciences and Mathematics|Physics|Engineering Physics", "engrXiv|Engineering|Risk Analysis", "engrXiv|Engineering|Other Engineering", "bepress|Engineering", "engrXiv|Engineering|Mechanical Engineering|Fluid Mechanics", "bepress|Engineering|Mechanical Engineering", "engrXiv|Engineering|Mechanical Engineering", "bepress|Engineering|Mechanical Engineering|Applied Mechanics", "Gaussian Process Regression", "02 engineering and technology", "7. Clean energy", "Virtual sensing", "wind turbine", "bepress|Engineering|Computational Engineering", "engrXiv|Engineering|Civil and Environmental Engineering", "0202 electrical engineering", " electronic engineering", " information engineering", "uncertainty", "Fatigue", "wake", "engrXiv|Engineering|Civil and Environmental Engineering|Structural Engineering", "Uncertainty", "engrXiv|Engineering|Mechanical Engineering|Applied Mechanics", "Bayesian Calibration", "engrXiv|Engineering|Engineering Physics", "bepress|Engineering|Risk Analysis", "engrXiv|Engineering", "bepress|Engineering|Civil and Environmental Engineering", "engrXiv|Engineering|Computational Engineering", "Wake", "bepress|Engineering|Aerospace Engineering|Aerodynamics and Fluid Mechanics", "bepress|Engineering|Civil and Environmental Engineering|Structural Engineering", "fatigue", "bepress|Engineering|Other Engineering", "Sensitivity analysis", "Wind turbine", "Bayesian Gaussian process regression"]}, "links": [{"href": "https://doi.org/10.1016/j.renene.2021.02.003"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Renewable%20Energy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.renene.2021.02.003", "name": "item", "description": "10.1016/j.renene.2021.02.003", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.renene.2021.02.003"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-11-05T00:00:00Z"}}, {"id": "10.2749/vancouver.2017.0809", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-22T16:19:10Z", "type": "Journal Article", "created": "2021-02-09", "title": "Surrogate Modelling For Fatigue Damage of Wind-Turbine Blades Using Polynomial Chaos Expansions and Non-Negative Matrix Factorization", "description": "A computational approach for the estimation of fatigue degradation of composite wind turbine blades by means of time domain aero-servo-elastic simulations is proposed. Wind turbine blades are subjected throughout their lifetime to highly stochastic loading. Fatigue damage of the composite reinforcement of the wind turbine blades has been identified early on in the wind turbine design practice as a factor driving design. A simple fatigue accumulation model is utilized for the spar cap reinforcement of a wind-turbine blade. Non-Negative Matrix Factorization (NMF) for the damage accumulation random field is used for dimensionality reduction. An approximate computationally efficient model, relying on Polynomial Chaos Expansion (PCE) of the damage state with respect to probabilistically modelled mean wind and turbulence intensity is derived. The framework is exemplified in a case-study of a 1.5MW wind turbine.
", "keywords": ["thin walled composite beam", "Composite fatigue", "long term fatigue assessment", "Aero-elastic simulation", "Gram-Schmidt PCE", "Wind Turbine", "0202 electrical engineering", " electronic engineering", " information engineering", "fatigue degradation", "NMF", "02 engineering and technology", "7. Clean energy", "0201 civil engineering"], "contacts": [{"organization": "Mylonas, Charilaos, Abdallah, Imad, Chatzi, Eleni,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.2749/vancouver.2017.0809"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/IABSE%20Reports", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.2749/vancouver.2017.0809", "name": "item", "description": "10.2749/vancouver.2017.0809", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.2749/vancouver.2017.0809"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-01-01T00:00:00Z"}}, {"id": "10.3929/ethz-b-000404307", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-22T16:19:35Z", "type": "Journal Article", "title": "Data\u2013Driven Remaining Useful Life Prediction for Anchor Fatigue", "keywords": ["autoencoder", "fatigue damage", "Technology (applied sciences)", "Machine learning; fatigue damage; dimensionality reduction; autoencoder", "Machine learning", "info:eu-repo/classification/ddc/600", "dimensionality reduction"], "contacts": [{"organization": "Mylonas, Charilaos, Abdallah, Imad, Vieira, Debora, Moisi, Kleidi, Zientek, Michal, Chatzi, Eleni; id_orcid0000-0002-6870-240X,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.3929/ethz-b-000404307"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/38th%20IMAC%20Conference%20and%20Exposition%20on%20Structural%20Dynamics%3A%20It%27s%20Not%20Just%20Modal%20Anymore%20%28IMAC%202020%29%2C%20Houston%2C%20TX%2C%20USA%2C%20February%2010-13%2C%202020", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3929/ethz-b-000404307", "name": "item", "description": "10.3929/ethz-b-000404307", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3929/ethz-b-000404307"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-01-01T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=fatigue&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=fatigue&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "first", "title": "items (first)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=fatigue&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=fatigue&offset=6", "hreflang": "en-US"}], "numberMatched": 6, "numberReturned": 6, "distributedFeatures": [], "timeStamp": "2026-09-22T18:52:27.647323Z"}