Control of damage‐sensitive features for early failure prediction of wind turbine blades-Reference-Cited by-同舟云学术

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Control of damage‐sensitive features for early failure prediction of wind turbine blades

Published:2021-09-25 Issue:1 Volume:29 Page:
ISSN:1545-2255
Container-title:Structural Control and Health Monitoring
language:en
Short-container-title:Struct Control Health Monit

Author:

Janeliukstis Rims¹^ORCID,McGugan Malcolm¹^ORCID

Affiliation:

1. Department of Wind Energy Technical University of Denmark Roskilde Denmark

Funder

Energistyrelsen

Publisher

Wiley

Subject

Mechanics of Materials,Building and Construction,Civil and Structural Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/stc.2852

Reference30 articles.

1. Algorithm for damage detection in wind turbine blades using a hybrid dense sensor network with feature level data fusion

2. Damage and ice detection on wind turbine rotor blades using a three-tier modular structural health monitoring framework

3. Automatic Detection of Wind Turbine Blade Surface Cracks Based on UAV-Taken Images

4. Hyperspectral imaging applied for the detection of wind turbine blade damage and icing

5. A modified composite fatigue damage model considering stiffness evolution for wind turbine blades

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1. Compression–compression fatigue damage of wrinkled carbon/glass hybrid composite laminates;Composite Structures;2024-10

2. Estimating the Azimuth of Acoustic Emission Source in Concrete Plate-Like Structures using a Non-Contact Sensor Unit;Acoustical Physics;2024-02

3. Damage identification in concrete structures using a hybrid time–frequency decomposition of acoustic emission responses;Journal of Civil Structural Health Monitoring;2023-10-30

4. Quantification of Statistical Error in the Estimate of Strain Power Spectral Density Transmissibility for Operational Strain Modal Analysis;Structural Control and Health Monitoring;2023-08-29

5. Feasibility study on a full‐scale wind turbine blade monitoring campaign: Comparing performance and robustness of features extracted from medium‐frequency active vibrations;Wind Energy;2023-08-10

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