As maintenance strategy becomes a central concern in the development of wind energy infrastructure, ensuring the reliability and availability of wind turbines requires increasingly accurate structural models. The growing use of sensors, including those embedded in the blades, provides access to rich vibration data that can be exploited to monitor the structural state of turbines in operation. In this context, model updating —the process of refining numerical model parameters using measurement data —plays a key role not only in improving model accuracy but also in structural health monitoring, enabling the detection and characterization of faults.

However, the structural complexity of modern wind turbines, combined with numerous uncertain or unknown parameters, makes accurate predictive modelling challenging. Moreover, the time-periodic dynamics induced by rotor rotation violate key assumptions of classical system identification techniques, which are based on Linear Time-Invariant (LTI) formulations. As a result, model updating approaches for rotating wind turbines remain limited.

This study proposes a dedicated framework for model updating of rotating wind turbines based on an equivalent Linear Time-Invariant (LTI) system, derived from a Fourier decomposition of the Floquet modes of the rotating system. This equivalent LTI system allows classical Operational Modal Analysis (OMA) techniques to be directly applied to measured time series, even for time-periodic systems. The modes identified through this approach correspond to those of the equivalent LTI system. By comparing the measured and simulated modes within a cost function, the parameters of the numerical model can be updated to better match the real structure. The full framework is summarized in the corresponding schematic.

The main contribution of this study is the introduction of a novel model updating framework for rotating wind turbines, enabling the estimation of blade parameters and thus the detection of potential structural faults during operation. This approach makes it possible to apply classical identification techniques to systems with time-periodic dynamics. It therefore allows to improve digital twin accuracy and enable condition-based maintenance in wind energy systems.

To demonstrate its effectiveness, the method is first validated on a simplified 5 Degree of-Freedom (DoF) wind turbine model, showing its ability to accurately update individual blade stiffness parameters using synthetic vibration data. Building on this preliminary case, the framework is then applied to the NREL 5 MW reference turbine, simulated with the aeroelastic code OpenFAST, involving about 80 DoF. This large-scale application enables testing under realistic and computationally demanding conditions. Challenges include noisy, multi-sensor data and reduced interpretability of the LTI equivalence. This study illustrates that the proposed approach is effective for analyzing complex turbine models and monitoring blade health in operation.

Structural Health Monitoring (SHM) systems, comprising complex assemblies of ICs, optical fibers, and packaging, generally rely on established standards such as MIL-HDBK-217, JEDEC, or FIDES for reliability modeling. However, the MTTF and FIT metrics derived from these standards are explicitly limited to random failures during the useful life period, ignoring the critical impact of aging.

To ensure reliable operation over extended timescales (typically 20 to 30 years), this study argues that wear-out must be addressed through a distinct analytical approach. We propose a methodology based on the Matrix framework recently formalized by Bernstein et al. (“Reliability Prediction for Microelectronics”, Wiley series, 2024). Instead of relying on constant failure rates, this approach isolates individual degradation mechanisms—modeled via Lognormal or Weibull b > 1) distributions as appropriate to their specific physics—and intelligently combines them. This method provides a more accurate prediction of system lifetime by capturing the cumulative effect of concurrent, competing failure modes. To illustrate this, a proof-of-concept was conducted on a 45 nm FinFET FPGA (Xilinx technology). Testing three failure mechanisms across nine DOE configurations (1.2–3 V; 10 MHz–1 GHz; −60 °C–155 °C) yielded the comprehensive Matrix-based physics-of-failure model presented in Figure 1.

This multi-mechanism paradigm, is extended to SHM systems. This empirical foundation justifies systematic mapping of Peck (humidity), Fick (diffusion), Arrhenius (thermal), Coffin-Manson (thermo-mechanical fatigue), and Paris (crack propagation) models—whose coupling effects are captured through experimentally calibrated linear combinations tailored to each material and assembly context.

For SHM architectures, environmental stressors—temperature extremes and cycling, humidity and salinity (offshore wind turbines), and vibration spectra—are directly mapped to their respective physics-based models. Fiber Bragg grating (FBG) sensors embedded in the SHM chain provide a real-time degradation signature via spectral observables—wavelength shift, reflectivity, bandwidth, and line-shape—that directly encode the combined effects of thermally induced effect, residual stress accumulation, microstructural embrittlement, and interfacial degradation. This integration of spectral indicators into the multi-mechanism reliability framework translates underlying physics-of-failure models into actionable health metrics, enabling quantitative assessment of cumulative wearout over the sensor's full operational lifetime.

Our approach leverages numerical modeling from the design phase to identify sensor weaknesses by analyzing frequencies, geometric features, and material interfaces. This enables prediction of degradation mechanisms under combined stresses and allows early anticipation of failures through Multiphysics simulations. We validate this methodology on a functional SHM demonstrator system exposed to various combined environments, monitoring performance signatures across a wide range of stress conditions to support extrapolation to realistic operational contexts. We also address the critical issue of ensuring that accelerated or extrapolated tests are truly representative. Reliable lifetime prediction using such data requires thorough calibration, validated acceleration laws, and a quantified assessment of projection uncertainty. By integrating experimentally calibrated, multi-stress wearout models mapped to sensor signatures, we can deliver not only MTTF and RUL estimates, but also real-time health indices for proactive SHM system management.

Wind turbine blades are subject to complex mechanical and environmental loads during operation. As rotor blades continue to grow in size, the design and manufacturing process becomes increasingly challenging, thereby increasing the likelihood of defects such as delamination, porosity, and fibre waviness. Structural weaknesses in adhesive joints, transition zones, and sandwich structures, along with operational damage such as erosion, lightning strikes, fatigue, and debonding, are frequently observed.

To reduce the risk of extensive repair efforts and prolonged turbine downtime, it is crucial to identify structural damage in rotor blades at an early stage. A promising approach is the use of embedded sensors for continuous monitoring of damage-related key performance indicators (KPIs). However, implementing such systems requires a fundamental understanding of how specific damage types affect these KPIs. Since detailed simulations are often not feasible for operators due to limited design data, cost-effective investigations using decommissioned blades offer a practical first step toward developing and validating monitoring strategies.

In this study, experimental tests were conducted on two 44-meter decommissioned rotor blades to assess the influence of structural damage on their dynamic behaviour. The blades were tested in a stationary setup, each supported on two pallets near the blade root and two pallets at a radius of 30 metres.

The site’s availability was limited and came on short notice. As a result, only a minimal sensor setup consisting of accelerometers was used for the investigation. Each blade was excited in both flap-wise and edgewise directions using three excitation methods: small hammer, large hammer, and manual excitation. Structural damage was introduced sequentially, with measurements taken before and after each damage state. Accelerations were measured at five fixed sensor positions distributed across the blade, with additional accelerometers placed near damage sites, to examine how damage sensitivity varies with sensor distance. In total, six different damage types were investigated, three on each blade. Due to the limited sensor setup, the excitation was quantified by measuring hammer acceleration, which limited the derivation of traditional frequency response functions and prevented full modal analysis. Nevertheless, acceleration input-output relations were investigated, focusing on frequency shifts of relevant peaks, especially in the low-frequency domain, and broadband amplitude shifts, particularly in the higher-frequency domain.

The investigation provides a broad characterisation of blade dynamics under various damage conditions. While the test setup does not replicate the boundary conditions of an operational turbine, it serves as a starting point for developing early damage detection strategies. The transferability of the results to an operational turbine requires further validation. Additionally, the use of the minimal sensor setup based on acceleration measurements did not allow for a comprehensive modal analysis, including damping and mode shapes. However, it still enabled the detection of damage-related changes in dynamic behaviour and provides a first basis for developing KPIs. In particular, the methodology allowed the identification of frequency shifts and changes in the dynamic response, which may serve as early indicators of structural degradation.

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.