← Back to all news
arXiv Machine Learning By Philip Franz, Max von Danwitz, Gregory Duth\'e, Alexander Popp, Eleni Chatzi

Towards Interpretable Damage Detection based on Aerodynamic Pressure Measurements

Read the original on arXiv Machine Learning →

The paper presents a method for detecting and classifying damage in wind turbine blades using aerodynamic pressure measurements from the Aerosense system. A convolutional neural network was trained on data from a NACA 633418 airfoil mounted on a vibrating cantilever, where damage was introduced via saw cuts. The study also incorporates physics-based insights and explainable ML techniques to interpret how damage affects dynamic response and pressure fields, enhancing transparency and robustness of the monitoring pipeline.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

Hugging Face Trending Papers
Jun 25

A Multi-Fidelity Convolutional Autoencoder-Transfer Learning Framework for Guided-Wave-Based Damage Diagnosis Using Large Simulated and Limited Experimental Datasets

Guided wave-based structural health monitoring (GWSHM) with onboard transducers offers significant potential for the early diagnosis of damage in engineering structures. However, the practical deployment of deep learning models is often hindered by the limited availability of labelled experimental data and the high computational cost of generating large-scale high-fidelity simulation datasets.

fine-tuning
More like this →
arXiv Machine Learning
Jul 7

Transformer-based Multisensor Data Fusion of Ultrasonic Guided Wave and FBG-based Strain Measurements for Multitask Aerospace Structural Health Monitoring

arXiv:2607. 02545v1 Announce Type: cross Abstract: Structural health monitoring (SHM) has emerged as an essential tool for ensuring the integrity and reliability of critical engineering structures, particularly in aerospace applications.

By Xin Yang, Morteza Moradi, Tongtong Yan, Jinbo Du, Yunlai Liao, Dimitrios Zarouchas, Dimitrios Chronopoulos
llmsbenchmarks
More like this →
arXiv Machine Learning
Aug 10

Defining Energy Indicators for Impact Identification on Aerospace Composites: A Structured Feature Selection Approach Guided by Domain Knowledge

arXiv:2511. 01592v2 Announce Type: replace Abstract: Energy estimation is critical to impact identification on aerospace composites, where low-velocity impacts can induce internal damage that is undetectable at the surface.

By Nat\'alia Ribeiro Marinho, Richard Loendersloot, Frank Grooteman, Jan Willem Wiegman, Uraz Odyurt, Tiedo Tinga
ragefficiencysafety
More like this →