Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Wind Turbine Machine Models01:24

Wind Turbine Machine Models

562
In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...
562
Design Example: Calculating Safe Diameter for Wind-Exposed Disc01:17

Design Example: Calculating Safe Diameter for Wind-Exposed Disc

355
Assessing safety in wind-exposed installations is crucial to preventing potential failures. This example explores the calculation and design adjustments needed to mount a circular disc on a building facade, where wind forces are a primary concern. A 4-meter diameter disc was initially designed as an aesthetic feature facing winds at a velocity of 25 meters per second, with an air density of 1.25 kilograms per cubic meter. Given these conditions, the drag force on the disc was determined using...
355
Fatigue01:21

Fatigue

801
Fatigue occurs when materials rupture under repeated or fluctuating loads, even at stress levels far below their static breaking strength. It typically results in brittle failure, even for ductile materials. It is a critical consideration in designing machines and structural components subjected to repetitive or varying loads. The nature of these loadings can range from fluctuating loads like unbalanced pump impellers causing vibrations to repeatedly bending a thin steel rod wire back and forth...
801
Radial System Protection01:23

Radial System Protection

420
Radial systems employ time-delay overcurrent relays to reduce load interruptions. When a fault occurs, the nearest breaker opens first, while upstream breakers remain closed due to longer delay settings. This approach ensures minimal disruption to the rest of the system.
In a radial system with a fault downstream of the third breaker, ideally, only the third breaker will open, isolating the fault and interrupting the load connected beyond it. The second breaker has a longer delay setting,...
420
Node Analysis for AC Circuits01:14

Node Analysis for AC Circuits

642
Consider an angioplasty system featuring a catheter equipped with a turbine, a critical tool for removing plaque deposits from coronary arteries. This intricate medical device operates using a circuit model reminiscent of a dual-node RLC circuit powered by a current-controlled voltage source.
To unravel the complexities of this system, nodal analysis is employed, a powerful technique founded on Kirchhoff's current law (KCL), which remains valid for phasors. AC circuits can effectively be...
642

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Performance Characterization of Radar-Based Delamination Assessment in Glass Fiber Reinforced Composites.

Sensors (Basel, Switzerland)·2026
Same author

Explainable Machine Learning for Tower-Radar Monitoring of Wind Turbine Blades: Fine-Grained Blade Recognition Under Changing Operational Conditions.

Sensors (Basel, Switzerland)·2026
Same author

Monolithically Integrated THz Detectors Based on High-Electron-Mobility Transistors.

Sensors (Basel, Switzerland)·2025
Same author

Novel Methodologies for Multiaxial Strain Measurements with Piezoresistive Films based on Graphene Nanoplatelets.

Small science·2025
Same author

Tiny Machine Learning Implementation for Guided Wave-Based Damage Localization.

Sensors (Basel, Switzerland)·2025
Same author

Analytical and experimental analysis of guided waves in an aluminum plate under bending load.

Ultrasonics·2024
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jan 17, 2026

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

6.5K

Radar-Based Damage Detection in a Wind Turbine Blade Using Convolutional Neural Networks: A Proof-of-Concept Under

Erik Streser1, Sercan Alipek2, Manuel Rao2

  • 1Department of Physics, Goethe-University Frankfurt, Max-von-Laue Str. 1, 60438 Frankfurt am Main, Germany.

Sensors (Basel, Switzerland)
|September 19, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel convolutional neural network (CNN) approach for detecting damage in wind turbine blades using radar data. The method accurately classifies intact versus damaged conditions, achieving high F1-scores even under varying loads.

Keywords:
FMCW radarconvolutional neural networkdamage detectionmillimeter-wavestructural health monitoringwind turbine blade

More Related Videos

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

2.1K
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

2.3K

Related Experiment Videos

Last Updated: Jan 17, 2026

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

6.5K
Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

2.1K
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

2.3K

Area of Science:

  • Engineering
  • Materials Science
  • Artificial Intelligence

Background:

  • Structural health monitoring (SHM) is crucial for wind turbine blade integrity.
  • Conventional SHM methods often require complex compensation for environmental and operational factors.
  • Radar-based SHM offers a non-destructive and potentially more robust alternative.

Purpose of the Study:

  • To develop and validate a convolutional neural network (CNN)-based damage detection framework for wind turbine blades using radar measurements.
  • To evaluate the performance of the proposed CNN approach in distinguishing between intact and damaged blade conditions.
  • To demonstrate the framework's ability to inherently learn necessary information without explicit compensation for temperature and loading effects.

Main Methods:

  • Radar measurements from embedded sensors on a 31m wind turbine blade were collected during fatigue testing.
  • Radar data was transformed into an image-type representation suitable for CNN input.
  • A CNN model was trained to classify the condition of the wind turbine blade (intact vs. damaged).

Main Results:

  • The CNN-based approach demonstrated high accuracy in damage detection.
  • Achieved F1-scores for damage classification ranged from 91% to 100%.
  • The framework successfully classified blade conditions under both unloaded and loaded states, indicating robustness.

Conclusions:

  • The proposed CNN-based radar SHM approach is effective for wind turbine blade damage detection.
  • The framework's ability to learn damage indicators directly from radar images simplifies the SHM process.
  • This method shows significant promise for reliable and automated structural health monitoring of wind turbine blades.