ORIGINAL RESEARCH article
Front. Mech. Eng.
Sec. Turbomachinery
This article is part of the Research TopicAdvances in Condition Monitoring and Fault Diagnosis of Rotating Machinery: Model-based, Signal-based and Data-driven PerspectivesView all 3 articles
Guided Wave based Health Monitoring of Composite Wind Turbine Blades: Multi-Level Damage Assessment
Provisionally accepted
- 1Amrita Vishwa Vidyapeetham Amrita School of Engineering, Coimbatore, India
- 2University of Huddersfield, Huddersfield, United Kingdom
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Structural health monitoring (SHM) of wind turbines is critical for maintaining continuous operation, minimizing maintenance expenses, and maximizing energy production. Recent advancements in sensor technology have made it possible to gather extensive ultrasonic guided wave (UGW) data from wind turbine components, enabling assessment of their structural integrity. This research examines UGW-based nondestructive evaluation techniques applied to composite wind turbine blades under varied structural conditions using experimental and numerical analysis. UGW signals recorded through an actuator-sensor network contain essential information on blade health. A Random Forest model is used to predict changes in A0 and S0 mode group velocities and amplitudes due to erosion/corrosion, longitudinal debonding, and transverse debonding across damage sizes ranging from 0 to 40 mm. To assess prediction reliability, 95% confidence intervals are included as uncertainty bands; narrower bands suggest higher confidence, while a wider band indicates greater uncertainty. Sensitivity analysis highlights the impact of damage size and type on UGW signal properties, supporting improved predictions. This study underscores the potential of UGW-based SHM to enhance wind turbine reliability and promote sustainable energy generation.
Keywords: Composite wind turbine blade, damage detection, structural health monitoring, Ultrasonic guided waves, uncertainty quantification
Received: 02 Jul 2025; Accepted: 15 Dec 2025.
Copyright: © 2025 Pillai and Sikdar. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Shirsendu Sikdar
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