Machine Learning Techniques for Damage Detection in Wind Turbine Blades

Tavares, A. C.; Lopes, Bernardo; Lorenzo, Emilio Di; Cornelis, Bram; Peeters, Bart; Desmet, Wim; Gryllias, Konstantinos

doi:10.1007/978-3-031-07254-3_18

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…This results in a series of ACFs, which represent the classical TV ACF estimate. Th power spectrum estimates are provided by the discrete Fourier transform of the ACF The resonance frequencies and damping ratios are estimated and tracked with the help o the operational Polymax method [24,46].…”

Section: The Resultsmentioning

confidence: 99%

A Nonparametric Regularization for Spectrum Estimation of Time-Varying Output-Only Measurements

Csurcsia,

Ajmal,

De Troyer

2024

Vibration

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In this work, an advanced 2D nonparametric correlogram method is presented to cope with output-only measurements of linear (slow) time-varying systems. The proposed method is a novel generalization of the kernel function-based regularization techniques that have been developed for estimating linear time-invariant impulse response functions. In the proposed system identification technique, an estimation method is provided that can estimate the time-varying auto- and cross-correlation function and indirectly, the time-varying auto- and cross-correlation power spectrum estimates based on real-life measurements without measuring the perturbation signals. The (slow) time-varying behavior means that the dynamic of the system changes as a function of time. In this work, a tailored regularization cost function is considered to impose assumptions such as smoothness and stability on the 2D auto- and cross-correlation function resulting in robust and uniquely determined estimates. The proposed method is validated on two examples: a simulation to check the numerical correctness of the method, and a flutter test measurement of a scaled airplane model to illustrate the power of the method on a real-life challenging problem.

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Section: The Resultsmentioning

confidence: 99%

A Nonparametric Regularization for Spectrum Estimation of Time-Varying Output-Only Measurements

Csurcsia,

Ajmal,

De Troyer

2024

Vibration

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show abstract

“…Such a system is implemented as a data-driven method, based on a normal behavior modelling, for detection of temperature-related anomalies in turbine components. (It is important to note that we decided to implement a data-driven method because it has been proven that data-driven models can satisfactorily model complex systems such as wind turbines [39]) As a consequence, the methodology can be used for controlled experiments related to anomaly detection in components such as drivetrain, generators [32,40,41], blade pitch [42], and blades [43,44].…”

Section: Methodsmentioning

confidence: 99%

Discussion on the Suitability of SCADA-Based Condition Monitoring for Wind Turbine Fault Diagnosis through Temperature Data Analysis

et al. 2023

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Wind turbines are expected to provide on the order of 50% of the electricity worldwide in the near future, and it is therefore fundamental to reduce the costs associated with this form of energy conversion, which regard maintenance as the first item of expenditure. SCADA-based condition monitoring for anomaly detection is commonly presented as a convenient solution for fault diagnosis on turbine components. However, its suitability is generally proven by empirical analyses which are limited in time and based on a circumscribed number of turbines. To cope with this lack of validation, this paper performs a controlled experiment to evaluate the suitability of SCADA-based condition monitoring for fault diagnosis in a fleet of eight turbines monitored for over 11 years. For the controlled experiment, a weakly supervised method was used to model the normal behavior of the turbine component. Such a model is instantiated as a convolutional neural network. The method, instantiated as a threshold-based method, proved to be suitable for diagnosis, i.e. the identification of all drivetrain failures with a considerable advance time. On the other hand, the wide variability between the time the alarm is raised and the fault is observed suggests its limited suitability for prognosis.

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Monitoring Damage Progression in Wind Turbine Blade Under Fatigue Testing Using Acceleration Measurements

Rossi,

Tavares,

Di Lorenzo

et al. 2024

Lecture Notes in Civil Engineering

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Machine Learning Techniques for Damage Detection in Wind Turbine Blades

Cited by 3 publications

References 12 publications

A Nonparametric Regularization for Spectrum Estimation of Time-Varying Output-Only Measurements

A Nonparametric Regularization for Spectrum Estimation of Time-Varying Output-Only Measurements

Discussion on the Suitability of SCADA-Based Condition Monitoring for Wind Turbine Fault Diagnosis through Temperature Data Analysis

Monitoring Damage Progression in Wind Turbine Blade Under Fatigue Testing Using Acceleration Measurements

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