Detection of structural defects in wind turbine blades employing guided waves and machine learning methods

Wind turbine blades undergo high operational loads, experience variable environmental conditions, and are susceptible to failures due to defects, fatigue, and weather induced damage. These large-scale composite structures are essentially enclosed acoustic cavities and currently have limited, if any, structural health monitoring in practice. A novel acoustics-based structural sensing and health monitoring technique is developed, requiring efficient algorithms for operational damage detection of cavity structures. This paper describes a systematic approach used in the identification of a competent machine learning algorithm as well as a set of statistical features for acoustics-based damage detection of enclosed cavities, such as wind turbine blades. Logistic regression (LR) and support vector machine (SVM) methods are identified and used with optimal feature selection for decision making using binary classification. A laboratory-scale wind turbine with hollow composite blades was built for damage detection studies. This test rig allows for testing of stationary or rotating blades (each fit with an internally located speaker and microphone), of which time and frequency domain information can be collected to establish baseline characteristics. The test rig can then be used to observe any deviations from the baseline characteristics. An external microphone attached to the tower will also be utilized to monitor blade damage while blades are internally ensonified by wireless speakers. An initial test campaign with healthy and damaged blade specimens is carried out to arrive at certain conclusions on the detectability and feature extraction capabilities required for damage detection.

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Structural health monitoring for delamination detection and location in wind turbine blades employing guided waves

Wind Energy ◽

10.1002/we.2316 ◽

2019 ◽

Vol 22 (5) ◽

pp. 698-711 ◽

Cited By ~ 20

Author(s):

Carlos Quiterio Gómez Muñoz ◽

Fausto Pedro García Marquez ◽

Borja Hernandez Crespo ◽

Kena Makaya

Keyword(s):

Structural Health Monitoring ◽

Wind Turbine ◽

Health Monitoring ◽

Guided Waves ◽

Turbine Blades ◽

Wind Turbine Blades ◽

Structural Health ◽

Delamination Detection

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A Comparative Study for Condition Monitoring on Wind Turbine Blade using Vibration Signals through Statistical Features: a Lazy Learning Approach

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.10.20833 ◽

2018 ◽

Vol 7 (4.10) ◽

pp. 190 ◽

Cited By ~ 2

Author(s):

A. Joshuva ◽

V. Sugumaran

Keyword(s):

Machine Learning ◽

Wind Turbine ◽

Phase 1 ◽

Turbine Blades ◽

Computational Time ◽

Learning Approach ◽

Statistical Features ◽

Wind Turbine Blades ◽

Vibration Signals ◽

Machine Learning Approach

This study is to identify whether the wind turbine blades are in good or faulty conditions. If faulty, then the objective to find which fault condition are the blades subjected to. The problem identification is carried out by machine learning approach using vibration signals through statistical features. In this study, a three bladed wind turbine was chosen and faults like blade cracks, hub-blade loose connection, blade bend, pitch angle twist and blade erosion were considered. Here, the study is carried out in three phases namely, feature extraction, feature selection and feature classification. In phase 1, the required statistical features are extracted from the vibration signals which obtained from the wind turbine through accelerometer. In phase 2, the most dominating or the relevant feature is selected from the extracted features using J48 decision tree algorithm. In phase 3, the selected features are classified using machine learning classifiers namely, K-star (KS), locally weighted learning (LWL), nearest neighbour (NN), k-nearest neighbours (kNN), instance based K-nearest using log and Gaussian weight kernels (IBKLG) and lazy Bayesian rules classifier (LBRC). The results were compared with respect to the classification accuracy and the computational time of the classifier.

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Interpretable machine learning in damage detection using Shapley Additive Explanations

10.31224/osf.io/96yf5 ◽

2021 ◽

Author(s):

Artur Movsessian ◽

David Garcia Cava ◽

Dmitri Tcherniak

Keyword(s):

Machine Learning ◽

Damage Detection ◽

Wind Turbine ◽

Prediction Models ◽

Turbine Blades ◽

Wind Turbine Blades ◽

Lumped Mass ◽

Damage Indices ◽

Wide Range ◽

The Difference

In recent years, Machine Learning (ML) techniques have gained popularity in Structural Health Monitoring (SHM). These have been particularly used for damage detection in a wide range of engineering applications such as wind turbine blades. The outcomes of previous research studies in this area have demonstrated the capabilities of ML for robust damage detection. However, the primary challenge facing ML in SHM is the lack of interpretability of the prediction models hindering the broader implementation of these techniques. For this purpose, this study integrates the novel Shapley Additive exPlanations (SHAP) method into a ML-based damage detection process as a tool for introducing interpretability and, thus, build evidence for reliable decision-making in SHM applications. The SHAP method is based on coalitional game theory and adds global and local interpretability to ML-based models by computing the marginal contribution of each feature. The contribution is used to understand the nature of damage indices (DIs). The applicability of the SHAP method is first demonstrated on a simple lumped mass-spring-damper system with simulated temperature variabilities. Later, the SHAP method has been evaluated on data from an in-operation V27 wind turbine with artificially introduced damage in one of its blades. The results show the relationship between the environmental and operational variabilities (EOVs) and their direct influence on the damage indices. This ultimately helps to understand the difference between false positives caused by EOVs and true positives resulting from damage in the structure.

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Prediction of dynamic mooring responses of a floating wind turbine using an artificial neural network

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1201/1/012023 ◽

2021 ◽

Vol 1201 (1) ◽

pp. 012023

Author(s):

F A Bjørni ◽

S Lien ◽

T Aa Midtgarden ◽

G Kulia ◽

A Verma ◽

...

Keyword(s):

Machine Learning ◽

Wind Turbine ◽

Wave Height ◽

Significant Wave Height ◽

Learning Methods ◽

Mooring Lines ◽

Peak Period ◽

Significant Wave ◽

Machine Learning Methods ◽

Artificial Neural

Abstract Numerical simulations in coupled aero-hydro-servo-elastic codes are known to be a challenge for design and analysis of offshore wind turbine systems because of the large number of design load cases involved in checking the ultimate and fatigue limit states. To alleviate the simulation burden, machine learning methods can be useful. This article investigates the effect of machine learning methods on predicting the mooring line tension of a spar floating wind turbine. The OC3 Hywind wind turbine with a spar-buoy foundation and three mooring lines is selected and simulated with SIMA. A total of 32 sea states with irregular waves are considered. Artificial neural works with different constructions were applied to reproduce the time history of mooring tensions. The best performing network provides a strong average correlation of 71% and consists of two hidden layers with 35 neurons, using the Bayesian regularisation backpropagation algorithm. Sea states applied in the network training are predicted with greater accuracy than sea states used for validation of the network. The correlation coefficient is primarily higher for sea states with lower significant wave height and peak period. One sea state with a significant wave height of 5 meters and a peak period of 9 seconds has an average extreme value deviation for all mooring lines of 0.46%. Results from the study illustrate the potential of incorporating artificial neural networks in the mooring design process.

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A New Approach for Fault Detection, Location and Diagnosis by Ultrasonic Testing

Energies ◽

10.3390/en13051192 ◽

2020 ◽

Vol 13 (5) ◽

pp. 1192 ◽

Cited By ~ 6

Author(s):

Fausto Pedro García Marquez ◽

Carlos Quiterio Gómez Muñoz

Keyword(s):

Wind Turbine ◽

Wavelet Transforms ◽

Guided Waves ◽

Turbine Blades ◽

Environmental Parameters ◽

Wind Turbine Blades ◽

New Approach ◽

Abrupt Changes ◽

Different Types ◽

Cross Correlations

Wind turbine blades are constantly submitted to different types of particles such as dirt, ice, etc., as well as all the different environmental parameters that affect the behaviour and efficiency of the energy generation system. These parameters can cause faults to the wind turbine blades, modifying their behaviour due, for example, to the turbulence. A new method is presented in this paper based on cross-correlations to determine the presence of delamination in the blades. The experiments were conducted in two real wind turbine blades to analyse the fault and non-fault blades using ultrasonic guided waves. Finally, the energy analysis of the signal based on wavelet transforms allowed to determine energies abrupt changes in the correlation of the signals and to locate the faults.

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