Fuzzy finite element model updating for damage severity assessment - Turnbull, H; Omenzetter, P (Journal of physics. Conference series, 2024)
In general, monitoring of turbines remains a manual process, with inspections carried out at pre-defined intervals driving operation and maintenance costs prohibitively high. This research will develop a vibration-based structural health monitoring (VBSHM) methodology for remote monitoring and damage severity assessment of a laboratory-scale wind turbine blade under simulated wind-like excitation. The methodology will exploit the fact that structural degradation will manifest itself through a notable shift in pre-defined damage-sensitive features and use this to predict damage accrued on the structure. The finite element model updating (FEMU) procedure adopted leads to the creation of a “digital twin” by minimising a fitness function containing the discrepancy between model responses and observed dynamic responses. The application of deterministic FEMU can be considered idealistic, as uncertainty can have a non-negligible influence on the accuracy of the final solution. To this end, the authors incorporated non-probabilistic fuzzy theory, modelling membership functions of output parameters to build membership functions associated with input parameters. This accounts for limitations associated with determinism and enables modelling and measurement errors to be accounted for in a meaningful way. The method was demonstrated on a 2.36m blade from a 5kW domestic wind turbine subject to wind-like excitation. Operational modal analysis techniques were used to obtain dynamic responses of the structure with metaheuristic optimisation algorithms implemented to calibrate the numerical models using a modified version of the Abaqus2matlab toolbox. Through this process, a digital twin of the baseline structure was successfully constructed, with longitudinal modulus and shear modulus calibrated to reduce the maximum percentage deviation in natural frequencies from 19.4% to 1.4%. This calibrated model was then used as a baseline for further damage detection studies. To facilitate damage severity assessment non-destructively, two typically observed damages were considered. Localised stiffness reduction, comparable to transverse cracking, was replicated by adding small masses to the blade, whilst gradual boundary degradation was simulated through the addition of a neoprene sheet to increase joint flexibility. The VBSHM developed was able to detect with sufficient accuracy each simulated cracking scenario (0.20kg and 0.40kg on the blade’s trailing edge only and 0.20kg on both trailing and leading edges). The benefits of considering uncertainty were demonstrated through the creation of membership functions for each scenario to prevent false alarms and provide confidence in the results. This contribution highlights the ability to account for uncertainties in a non-computationally expensive and intuitive way and can be developed further to reduce O&M costs associated with in-service turbine blades. Boundary degradation was successfully identified experimentally; however the analytical sensitivity of responses to variation in rotational and translational springs was insufficient to facilitate updating using the analytical model created.