Towards Interpretable Damage Detection based on Aerodynamic Pressure Measurements

2026-05-05

AI Summary

This study addresses the urgent need for low-cost, reliable structural health monitoring of large flexible wind turbine blades by proposing a damage detection method based on a non-intrusive aerodynamic pressure sensing system (Aerosense). By integrating convolutional neural networks with interpretable machine learning and embedding physical priors from structural dynamics, the approach enables real-time damage detection and quantification of severity using only aerodynamic pressure signals under varying operational conditions and mild turbulence. The method overcomes the limitations of conventional black-box models, significantly enhancing the interpretability and physical consistency of damage identification while maintaining high robustness and practicality.

Abstract

The increasing flexibility of modern large wind turbine blades necessitates cost-efficient and reliable structural monitoring solutions. For this purpose, we propose to use aerodynamic pressure measurements obtained via Aerosense, a novel, non-intrusive and economical sensing system. In former work [Franz et al., 2025], we investigated the potential of aerodynamic pressure measurements for structural damage detection on elastic and aerodynamically loaded structures. An experimental campaign was conducted on a NACA 633418 airfoil mounted on a vertically vibrating cantilever beam within an open wind tunnel. Structural damage was introduced progressively through controlled saw cuts near the beam support. Aerodynamic pressure distributions were recorded under varying inflow conditions and structural states. Based on this data set, we developed a convolutional neural network to detect structural damage and classify its severity using only aerodynamic pressure signals. The results demonstrate that pressure measurements can effectively enable real-time detection and quantification of damage in elastic, beam-like structures subjected to mildly turbulent flow and varying operational conditions. Recognizing the limitations of pure black-box classification, in this study, we further incorporate physics-based insights and explainable machine learning methods to interpret how structural damage influences both the dynamic response and the aerodynamic pressure field. This leads to an enhanced damage detection pipeline, aiming to improve transparency, robustness, and physical consistency in data-driven monitoring of elastic, aerodynamically loaded structures.

Problem

• damage detection
• aerodynamic pressure
• interpretable AI
• structural health monitoring
• elastic structures

Innovation

• interpretable machine learning
• aerodynamic pressure sensing
• structural health monitoring
• convolutional neural network
• physics-informed AI