Damage Detection In Wind Turbine Blades Research Articles

An unsupervised damage detection method for an operating wind turbine blade

Wind turbine blades are normally exposed to significant dynamic loads over their lifetime, including extreme conditions. This can lead to several types of damages such as cracking and delamination. Monitoring the structural condition of the blades to detect damages before they become catastrophic can be important. In recent years, the concept of data-driven structural health monitoring (SHM) approaches for wind turbine blade damage detection has become popular. These methods employ features collected from time series obtained from monitored wind turbine blades to identify damages. However, selecting an optimum number of features, which can reduce computational cost and minimize the effects of environmental and operational conditions, is still a challenge. In this paper, a data-driven approach is developed combining a feature extraction strategy and un-supervised anomaly detection. Monitoring data collected from several accelerometers mounted on a blade of an operating Vestas V27 wind turbine in healthy and damaged scenarios are used in this regard. Several time-domain, temporal, and time-frequency domain features are employed for the purpose of anomaly detection using unsupervised learning methods. Isolation Forest and One-class Support Vector Machine are used to train for anomaly detection. A threshold of detection is established to distinguish between the healthy and damaged states. Results show that the proposed approach can successfully identify the damage cases, but environmental and operational conditions still have some effect when it comes to identifying the extent of the damage.

WTBD-YOLOv8: An Improved Method for Wind Turbine Generator Defect Detection

Wind turbine blades are the core components responsible for efficient wind energy conversion and ensuring stability. To address challenges in wind turbine blade damage detection using image processing techniques such as complex image backgrounds, decreased detection performance due to high image resolution, prolonged inference time, and insufficient recognition accuracy, this study introduces an enhanced wind turbine blade damage detection model named WTDB-YOLOv8. Firstly, by incorporating the GhostCBS and DFSB-C2f modules, the aim is to reduce the number of model parameters while enhancing feature extraction capability. Secondly, by integrating the MHSA-C2f module, which incorporates a multi-head self-attention mechanism, the focus on global information is enabled, thereby mitigating irrelevant background interference and reducing the impact of complex backgrounds on damage detection. Lastly, adopting the Mini-BiFPN structure improves the retention of features for small target objects in shallow networks and reinforces the propagation of these features in deep networks, thereby enhancing the detection accuracy of small target damage and reducing false negative rates. Through training and testing on the Wind Turbine Blade Damage Dataset (WTBDD), the WTDB-YOLOv8 model achieves an average precision of 98.3%, representing a 2.2 percentage point improvement over the original YOLOv8 model. Particularly noteworthy is the increase in precision from 93.1% to 97.9% in small target damage detection. Moreover, the total parameter count of the model decreases from 3.22 million in YOLOv8 to 1.99 million, marking a reduction of 38.2%. Therefore, the WTDB-YOLOv8 model not only enhances the detection performance and efficiency of wind turbine blade damage but also significantly reduces the model parameter count, showcasing its practical advantages in engineering applications.

Wind turbine blade damage detection and classification based on sound feature signal using machine learning

Wind turbine blades experience variable environmental conditions and are prone to failure due to fatigue and weather-induced damage. In this study, the aerodynamic sound signal of wind turbine blades is collected while in operation. Short-Time Fourier Transform method identified and used to analyze the periodic signal of the rotation of the wind turbine blades. The spectral centroid of the pulse signal and the variation of the corresponding three blades spectral centroid are extracted as the input features for supervised machine learning. Gaussian mixture model algorithm classified features and used with blades damage trend for structural health monitoring. Through the Gaussian mixture model established by using the mean value and standard deviation of the peak value of the spectral centroid, the results after the model test show the accuracy of the damaged blade judgment can reach 98 %. In addition, from the visual classification chart, it can be known that the blade will enter the damage state, and maintenance should be carried out in advance to avoid damage to the blade and maintain the stable operation of the wind turbine.

Early stage damage detection of wind turbine blades based on UAV images and deep learning

In response to the shortcomings of existing image detection algorithms in the early damage detection of wind turbine blades, such as insufficient applicability and unsatisfactory detection results, this paper proposes an improved DINO (DETR with improved denoizing anchor boxes for end-to-end object detection) model for wind turbine blade damage detection called WTB-DINO. The improvement strategy of the DINO model is obtained by collecting and analyzing unmanned aerial vehicle (UAV) daily inspection image data in wind farms. First, the lightweight design of DINO's feature extraction backbone is implemented to meet the requirement of fast and effective video inspection by drones. Based on this, the Focus down-sampling and enhanced channel attention mechanism are incorporated into the model to enhance the feature extraction ability of the Backbone for damaged areas according to the characteristics of wind turbine blade images. Second, a parallel encoder structure is built, and a multi-head attention mechanism is used to model the relationship between samples for each type of damage with uneven distribution in the dataset to improve the feature modeling effect of the model for less-sample damage categories. Experimental results show that the WTB-DINO model achieves a detection precision and recall rate of up to 93.2% and 93.6% for wind turbine blade damage, respectively, while maintaining a high frame rate of 27 frames per second. Therefore, the proposed WTB-DINO model can accurately and in real-time classify and locate damaged areas in wind turbine blade images obtained by UAVs.

An aeroacoustics-based approach for wind turbine blade damage detection

In this work, aimed at the development of an aeroacoustics-based wind turbine blade damage detection approach, the noise scattered from two airfoils with damage at the trailing edge or at the leading edge is investigated. Four trailing edge cracks (with width of 0.2, 0.5, 1.0 and 2.0 mm) and four leading edge erosion configurations (consisting of gouges and delamination) are investigated for a NACA 0018 and a DU96 W180 airfoil. Experiments are carried out under clean and turbulent inflow conditions. Acoustic measurements are performed in an anechoic wind tunnel with a microphone array. The trailing edge crack causes a tonal peak at trailing-edge-thickness-based Strouhal number approximately equal to Sth ∼ 0.1 under clean and low turbulence intensity inflow conditions (e.g. ∼4% in this study). For a higher turbulence intensity (e.g., ∼7%), the tonal peaks are not detectable. For the leading edge erosion case, under clean inflow conditions and minor damage levels, the amplitudes of the harmonics in the trailing edge noise spectra increase compared with the baseline. For moderate damage levels, the harmonics on the suction side shift to higher frequencies with lower amplitudes. For the highest damage levels, only broadband characteristics are present, where low-frequency contributions increase and high-frequency contributions decrease as the damage level increases. When introducing turbulent inflow, the leading edge impingement noise level decreases at medium-high frequency (above 1000 Hz) with increasing levels of erosion.

An unsupervised data-driven approach for wind turbine blade damage detection under passive acoustics-based excitation

Existing passive acoustics-based techniques for wind turbine blade damage detection lack the robustness and adaptability necessary for an operational implementation due to their physics- and model-based dependency. In contrast, this study develops an entirely unsupervised, data-driven damage detection technique. The novelty of the technique lies in (i) the development and comparison of spectral and cepstral-domain features for the robust characterization of the cavity-internal acoustics, (ii) the use of autoencoder networks to reduce the effects of non-stationary acoustic excitation, and (iii) the exclusion of labeled or damage-case data in the training set. The technique was successfully demonstrated on a wind turbine blade section inflicted with damage of various sizes, types, and locations, and subjected to airflow-induced passive acoustic excitation provided by a wind tunnel. Damage detection accuracy up to 99.82% was achieved for some damage types.

Efficient and Accurate Damage Detector for Wind Turbine Blade Images

The damage of wind turbine blades is one of the main problems restricting wind power development. Object detection can identify the damaged regions and diagnose the damage types. To handle the high-resolution wind turbine blade images, this article presents a novel efficient, and accurate damage detector (EADD) for wind turbine blade images. The proposed method adopts Single Shot MultiBox Detector (SSD) as the detection framework and offers an improved ResNet as the backbone. Firstly, the improved ResNet backbone uses dense connection blocks consisting of factorized depth-wise separable bottleneck (FDSB) and feature aggregation module (FAM), which makes the damage detection model more lightweight and has a faster detection speed. Secondly, the bidirectional cross-scale feature pyramid (BiFPN) is introduced into the proposed method to use multi-scale features fully and have more feature expression. In addition, data pre-processing, exponential moving average (EMA) and label smooth methods are utilized to improve the accuracy and robustness of the model. The experimental results on the wind turbine blade damage detection dataset show that our proposed method can achieve the best trade-off between detection accuracy and computation time compared with other competitive methods.

Wind turbine blade damage detection using an active sensing approach

The wind energy sector is one of the fastest growing parts of the clean energy industry. As the wind energy sector grows, so does an increasing concern for the damage detection of wind turbine blades. This paper proposes an active sensing approach by utilizing piezoceramic transducers as actuators and sensors. The influence of the crack quantity, location, length and depth on the wave propagation was experimentally studied. Sweep sine signals ranging from 1 khz to 50 khz were used as input signals for active sensing. The change in the energy that propagated through the cracks was verified as feasible in detecting crack-related damage. An innovative polar plot analysis method based on Fast Fourier transform was developed to compare the minuscule difference between the damage signals and the baseline signal. The polar plot was able to make apparent differences in both the magnitude and the phase of the signals, which could be correlated to crack depth and plane geometry, respectively, based on the observation of the damage.