Towards Interpretable Damage Detection based on Aerodynamic Pressure Measurements

TL;DR

This paper presents a physics-informed, interpretable machine learning approach using aerodynamic pressure measurements for real-time damage detection in wind turbine blades, enhancing transparency and robustness.

Contribution

It introduces an explainable damage detection pipeline combining CNNs with physics-based insights for better interpretability of aerodynamic pressure data.

Findings

Pressure measurements enable effective real-time damage detection.

The approach maintains robustness under turbulent flow and operational variations.

Explainability improves understanding of damage effects on aerodynamic pressure fields.

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.