Aug 2026· e-Journal of Nondestructive Testing· Vol 31· 0 citations
Abstract
In 2025, wind energy was the primary source of renewable electricity in the EU. As wind energy deployment continues, automatic inspection of wind turbines, especially rotor blades, has gained increasing interest for the economically competitive wind energy market. Therefore, this study introduces a three-stage framework for combined damage detection and localization in wind turbine rotor blades based on acoustic event detection. Bridging the gap between established SHM techniques analyzing either low-frequency vibrations or ultrasonic structure-borne sound waves, our methodology is based on airborne sound in the audible frequeny range. First, we apply an adaptive thresholding technique to extract short-term percussive sound signals, which are subsequently localized using Time Differences of Arrival. Finally, a spatio-temporal accumulation of the emitted acoustic energy is introduced as a damage indicator. Our evaluations on two large-scale rotor blade fatigue tests demonstrate that a configuration of only two microphones is sufficient to detect and coarsely localize structurally relevant damage within a 12-m-long blade segment. To the best of the authors' knowledge, such a favorable trade-off between sensor spacing and localization accuracy remains unprecedented among established structure-borne SHM techniques.
Wind energy, as a major contributor to the renewable energy sector, is receiving increasing attention to meet the growing demand for low-carbon electricity. The performance of a wind turbine can be significantly affected by various types of damage in its components, particularly the rotor blades. If left undetected, damage can develop rapidly under harsh operating conditions, potentially leading to severe failure, reduced power output, and considerable economic losses. However, the anisotropic properties and curved geometry of composite wind turbine blades make conventional time-domain methods less effective for reliable damage identification and localisation. To address this challenge, a methodology is developed that integrates deep learning with ultrasonic guided wave data analysis to enable accurate detection and classification of blade damage. An extensive numerical investigation is conducted, considering a range of damage locations and sizes across the blades. The samples are excited using a five-cycle Hanning-modulated sinusoidal pulse, and the structural responses at selected sensing positions are captured and converted into time-frequency representations to reveal more damage-sensitive features. The image-like data are then analysed using a custom-designed deep learning model to categorise and localise different damage cases. The results demonstrate a high level of accuracy in detecting both the location and severity of damage, indicating the strong potential of this approach for large-scale application in smart structural health monitoring of wind turbines.
Farbod Dadashbaki, S. Sikdar, D. D. Mandal et al.· e-Journal of Nondestructive...· 0 citations
Wind turbines (WT) need to perform well to meet the ever-growing demand of green energy. However, the performance of a wind turbine can be jeopardized due to occurrence of damage within its components. Acoustic emission (AE) is highly sensitive to occurrence of damage and can be used for damage identification. However, the localization of an AE source is challenging using a time domain method for structures that involve material anisotropy and complex geometry (e.g., curved surfaces and variable thickness). With this view, this work presents an unsupervised framework that operates on narrow frequency bands (NFBs) for artificial AE source determination in composite wind turbine blades. Unlike the conventional time-domain methods that require a network of sensors and the time of arrival information, the new method requires only one sensor and the information contained in the reduced frequency bands within each AE signal to discern its source. The unsupervised framework is found to be highly efficient in finding pattern in the multi-dimensional frequency domain dataset extracted from AE signals and can easily cluster the AE signals as per their zone of occurrence in a WT blade. This method can be easily implemented in real and complex structures as it requires only one sensor. Due to the very high degree of accuracy, the method can be applied to real complex systems and structures where time domain methods are not feasible. The method can also be included in a digital twin model for accurate prediction of an AE source.
D. D. Mandal, S. Sikdar, Rakesh Mishra· e-Journal of Nondestructive...· 0 citations
The blades directly affect the safety and power generation efficiency of the wind turbines. With the blade size increases, the reliable modal identification becomes important for vibration-based health monitoring. Although operational modal analysis (OMA) technique has been used in condition monitoring for the wind turbine blades, most existing studies focus on investigating a specific single method or under ideal excitation conditions. To overcome this limitation, this study takes the IEA-15MW large wind turbine blade as the research object and compares three OMA methods through numerical simulations, namely covariance-driven stochastic subspace identification (SSI-COV), frequency domain decomposition (FDD), and poly-reference least squares complex frequency domain (PolyMAX). The performance of the modal parameter identification methods is evaluated with respect to different sensor layouts, blade–tower coupling conditions, and environmental excitations. The results indicate that sparse sensor deployment cannot reliably identify the damage-sensitive high-order and complex modes. A nine-channel layout concentrated near second-order deformation regions significantly improves the identification of second-order flapwise frequencies and controls the average error of the first six modes within 3%. PolyMAX shows the best identification stability under different numbers and layouts of the sensors. Blade–tower coupling changes the blade modal characteristics and increases identification difficulty. Under this condition, FDD can still identify both low-order and high-order modes with good stability. Under different real wind conditions, the increasing wind speed causes the aerodynamic load to deviate from the white noise assumption, generally leading to fluctuations in the identification errors, with relatively large local errors occurring at certain medium and high wind speeds. Overall, the three OMA methods show different advantages under different identification conditions. PolyMAX shows the best stability under different sensor layouts and performs best when wind speed increases in the coupled wind turbine model, indicating that it is the most suitable for the actual complex coupling effects and environmental conditions. This research hopefully provides a basis for the subsequent engineering application of vibration-based modal identification of large offshore blades.
Qiang Liu, Meng Zhang, Xu Han et al.· Energies· 0 citations
Complex wind farm environments cause severe spatial aliasing and signal attenuation in blade acoustic signature monitoring. This paper presents an acoustic sensor array topology optimization method based on multi-physics simulation for high-fidelity acquisition of weak voiceprint features. A three-dimensional sound field model coupling aerodynamic noise and mechanical vibration quantifies sound propagation under varying wind speeds and yaw angles. A heuristic particle swarm algorithm discretely optimizes microphone array coordinates on tower and nacelle surfaces by maximizing the signal-to-noise ratio. A surrogate model accelerates sound field evaluation while eliminating nodes disturbed by strong wind vortices. Experiments on a public blade crack acoustic dataset show that the optimized array reduces normalized root mean square error by 44.4 percent compared to conventional spiral arrays, and the proposed attention-based multi-scale network achieves an area under the curve of 0.967 and recall of 0.913.
Bihua Zou, Jie Liu· International Journal for Si...· 0 citations
The continuous increase in the number of wind turbines is one of the major innovations of the 21st century worldwide for generating renewable energy and simul taneously reducing greenhouse gases. Since maintenance by mechanics only takes place at intervals without continuous monitoring and is associated with high profit losses due to shutdowns, intelligent sensors and algorithms are used to monitor the entire structure, in particular wind turbine blades (WTBs). The field study by Maelzer et al. [1] and the fatigue test in the laboratory by Simon et al. [2] have shown that a radar-based approach in the millimeter-wave frequency range for structural health monitoring of WTBs is promising.
This work describes the overall system design of a radar-based SHM system and its field deployment in a wind turbine. For this purpose, four sensor boxes are mounted on the main web of two rotor blades of a wind turbine. Each sensor box contains a frequency-modulated continuous wave (FMCW) radar at 60GHz and an acceleration sensor. Furthermore, one control unit per blade is mounted in the hub. Eachsensor box is connected to the corresponding control unit using the Power over Dataline standard for independent communication [3]. One of the three control units is connected to the mobile network for data transfer. A reference damage model developed by Rao et al. [4] and previously implemented on a WTB segment is applied here to simulate an artificial delamination. Initial measurement results are presented taking the changing environmental and operational conditions of the wind turbine into account.
References
[1] MORITZ MÄLZER, SEBASTIAN BECK, SERCAN ALIPEK, ELIAS REICHART, JOCHEN MOLL, VIKTOR KROZER, CHRISTOS OIKONOMOPOULOS, JÜRGEN KASSNER, MANFREDHÄGELEN,THOMASHEINECKE,etal. Radar-based struc tural monitoring of wind turbines blades: Field results from two operational wind tur bines. STRUCTURAL HEALTH MONITORING 2023, 2023.
[2] Jonas Simon, Thomas Kurin, Jochen Moll, Oliver Bagemiel, Raphael Wedel, Stefan Krause, Fabian Lurz, Andreas Nuber, Vadim Issakov, and Viktor Krozer. Embedded radar networks for damage detection in wind turbine blades: Validation in a full-scale fatigue test. Structural Health Monitoring, 22(6):4252–4263, 2023.
[3] Tobias Huemmer, Thomas Kurin, Moritz Maelzer, Katharina Fiedler, Sebastian Beck, Jochen Moll, and Fabian Lurz. System architecture of a radar-based structural health monitoring system for wind turbine blades using power over dataline. In IEEE Sensors 2025. IEEE, 2025.
[4] Manuel E Rao, Jochen Moll, Peter Kraemer, and Viktor Krozer. Experimental application of a reversible reference damage model for radar-based shm of glass fiber reinforced polymer structures. In 2025 IEEE 12th International Workshop on Metrology for AeroSpace (MetroAeroSpace), pages 780–784. IEEE, 2025.
Moritz Maelzer, Sebastian Beck, Tobias Huemmer et al.· e-Journal of Nondestructive...· 0 citations
The rapid expansion of offshore wind farms (OWFs) is reshaping coastal soundscapes through operational turbine noise, while the acoustic responses of soniferous fishes within operational wind-farm areas remain difficult to assess using conventional fishery surveys. In this study, passive acoustic monitoring (PAM) was deployed within the Yangjiang Offshore Wind Farm to examine short-term associations among underwater sound fields, turbine spatial layout, a turbine startup event, and putative fish-call activity. Fish-call detections were lower along the transect with 550 m turbine spacing than along the 1300 m transect, coinciding with higher measured sound pressure levels in the 550 m spacing layout. Acoustic activity in the central zone of the 1300 m array was 6.46 times higher than that of the 550 m layout, suggesting that lower-noise areas within wind farms may provide favorable acoustic conditions for detectable fish-calling activity. During a turbine startup event, fish-call detection rates temporarily decreased during the transient increase in sound pressure level and subsequently recovered as the underwater noise returned toward baseline conditions. These patterns indicate that PAM can capture short-term soundscape-associated variation in fish-calling activity or call detectability. We further propose a conceptual trade-off framework in which foundation-associated habitat benefits and operational noise may jointly shape the acoustic space use of soniferous fishes in OWFs.
