Surrogate Modelling For Fatigue Damage of Wind-Turbine Blades Using Polynomial Chaos Expansions And Non-Negative Matrix Factorization

Mylonas, Charilaos; Abdallah, Imad; Chatzi, Eleni

doi:10.2749/vancouver.2017.0809

Cited by 3 publications

(4 citation statements)

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“…In Murcia et al, 12 non‐linear regression with orthogonal basis functions was applied as a predictive model ( Polynomial Chaos Expansions ‐ PCE) for damage equivalent loads. In Mylonas et al 13 PCE was employed together with linear dimensionality reduction, that is, principal components analysis (PCA) and non‐negative matrix factorization, for the purpose of treating the high dimensionality of cross‐section finite element analysis results. That work can be seen as an application of the “Dimensionality Reduction Surrogate Modeling” (DRSM) framework 14 .…”

Section: Prior Related Work On Machine Learning and Probabilistic Techniques For Damage Monitoring And Remaining Useful Life Predictionmentioning

confidence: 99%

Conditional variational autoencoders for probabilistic wind turbine blade fatigue estimation using Supervisory, Control, and Data Acquisition data

2021

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Wind turbine fatigue estimation is based on time‐consuming Monte Carlo simulations for various wind conditions, followed by cycle‐counting 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‐element 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‐linear mechanics, and control. A Variational Auto‐Encoder (VAE) is trained in order to model the probability distribution of the accumulated fatigue on the root cross‐section of a simulated wind turbine blade. The VAE is conditioned on historical data that correspond to coarse wind‐field measurement statistics, such as mean hub‐height wind speed, standard deviation of hub‐height wind speed and shear exponent. In the absence of direct measurements of structural loads, the proposed technique finds applications in making long‐term 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.

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Section: Prior Related Work On Machine Learning and Probabilistic Techniques For Damage Monitoring And Remaining Useful Life Predictionmentioning

confidence: 99%

Conditional variational autoencoders for probabilistic wind turbine blade fatigue estimation using Supervisory, Control, and Data Acquisition data

2021

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“…Training data sets are generated offline via a set of finite element models (FEMs) accurately reproducing the system of interest by varying the crack state variables. Similar approaches are illustrated in the literature, 24,30,31 where surrogate models are used to advance repeated computational procedures. Surrogate models are also exploited for the particles RUL estimates, by providing the crack stress intensity factors (SIFs) -one for each crack tip -as a function of the crack state variables.…”

Section: Introductionmentioning

confidence: 96%

“…17–20 PF has been also successfully employed in several situations for condition assessment of vibrating systems; meaningful examples are described in the literature. 21–24…”

Section: Introductionmentioning

confidence: 99%

“…[17][18][19][20] PF has been also successfully employed in several situations for condition assessment of vibrating systems; meaningful examples are described in the literature. [21][22][23][24] In the literature, diagnostics and prognostics are usually performed separately, relying on dedicated tools. More specifically, the PF is often confined to prognostics only, and its diagnostic capabilities are often neglected.…”

Section: Introductionmentioning

confidence: 99%

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Fatigue damage diagnosis and prognosis of an aeronautical structure based on surrogate modelling and particle filter

Cristiani

Sbarufatti

Cadini

et al. 2020

Structural Health Monitoring

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A key issue affecting the performances of every human-conceived engineering system is its degradation, fatigue crack growth being one of the major structural deterioration phenomena. Fatigue crack growth is usually modelled as a stochastic process: uncertainty sources lie both in the item and in the physical degradation process variability. Fatigue crack growth deserves close attention, especially considering that condition-based maintenance methodologies are recently experiencing a major drive to increase their technology readiness level, requiring validated diagnostic and prognostic methodologies which should be capable of operating online and in real-time. In this regard, particle filters provide a consistent Bayesian framework, where the posterior distribution of the system degradation state is recursively approximated based on a time-growing stream of observations measuring the system response, enabling, in general, increasingly informed lifetime estimates. However, the real-time operation capability of such methods is hindered by their requirements in terms of computational power, which is mainly due to the complexity of the structural models they rely upon. Within this work, a comprehensive particle filter framework, able to deal with fatigue crack growth uncertainty sources while simultaneously addressing the computational burden issue, is proposed. The algorithm structure enables to simultaneously perform the diagnosis and prognosis of fatigue crack growth, while the adoption of the augmented state formulation allows to address scenarios where the degradation process of fatigue crack growth fails to meet the degradation model ruling the particle filter. Artificial neural networks–based surrogate modelling is adopted at different stages and embedded within the particle filter algorithm, relieving the computational burden associated with the evaluation of the trajectory likelihoods as well as enabling a fast estimation of the remaining useful life. Both simulated and experimental data sets regarding fatigue crack growth in an aluminium aeronautical panel are used for the algorithm testing, additionally proving the validity and effectiveness thereof by means of common prognostic performance metrics.

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Spinning finite element analysis of longitudinally stiffened horizontal axis wind turbine blade for fatigue life enhancement

Sajeer

Mitra

Chakraborty

2020

Mechanical Systems and Signal Processing

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Surrogate Modelling For Fatigue Damage of Wind-Turbine Blades Using Polynomial Chaos Expansions And Non-Negative Matrix Factorization

Cited by 3 publications

References 20 publications

Conditional variational autoencoders for probabilistic wind turbine blade fatigue estimation using Supervisory, Control, and Data Acquisition data

Conditional variational autoencoders for probabilistic wind turbine blade fatigue estimation using Supervisory, Control, and Data Acquisition data

Fatigue damage diagnosis and prognosis of an aeronautical structure based on surrogate modelling and particle filter

Spinning finite element analysis of longitudinally stiffened horizontal axis wind turbine blade for fatigue life enhancement

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