Acoustic Emission (AE) Source Monitoring in Composite Wind Turbine Blades using Narrow Frequency Bands and Machine Learning

Guided Wave based Damage Characterisation and Localization in Composite Wind Turbine Blades

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.

Damage Localization in Wind Turbine Rotor Blades using Acoustic Event Detection

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.

On the Vibration-Based Modal Parameter Identification of Large Wind Turbine Blades

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.

Acoustic Emission Monitoring of S.A. Agulhas II: Parametric Analysis and Correlation with Global Strain

The long-term structural integrity of ships operating in harsh environments depends on the ability to detect and interpret early signs of fatigue damage under service conditions. Among available non-destructive techniques, Acoustic Emission (AE) monitoring has shown significant potential for detecting crack activity and local degradation processes in real time. However, the interpretation of AE data in full-scale environments remains complex, as ship structures are subjected to continuously varying mechanical, operational and environmental conditions. Demonstrating AE feasibility in realistic ship environments is therefore critical to advancing its integration within structural health monitoring frameworks. This study presents results from an AE monitoring campaign conducted on board the Polar Supply and Research Vessel S. A. Agulhas II (SAAII) shown in Figure 1 (left), owned by the Department of Forestry, Fisheries and the Environment of South Africa. Two groups of AE sensors were installed: one on the port side near a visually inspected fatigue crack, as shown in Figure 1 (right), and one on the starboard side in a region with a previously repaired crack. This configuration enabled a relative assessment of AE behavior between an active and a repaired region subjected to comparable loading conditions during a single voyage. In total, more than four million AE signals were collected, approximately 80% of which originated from the port-side sensor group, with amplitudes predominantly between 50 and 70 dB and exhibiting AE characteristics consistent with those observed in previous laboratory experiments. The monitoring campaign additionally benchmarked the insights from AE measurements in combination with global structural measurements, including strain gauges and accelerometers, whilst considering the ship route and weather information. This combination of data enables an evaluation of how AE activity correlates to operational and environmental parameters. The study reflects on aspects of sensor deployment, data acquisition, and correlation strategies with other measurements, demonstrating the feasibility and practical considerations of integrating AE monitoring within a multimodal sensing framework for in-service structural monitoring of steel ship structures.

Initial Results for In-situ Structural Health Monitoring of Wind Turbine Blades using an FMCW Radar Network at 60GHz

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.

Single-sensor Acoustic Emission Damage Location in Orthotropic Steel Decks Based on Continuous Wavelet Transform and Convolutional Neural Network