Structural health monitoring of a composite wind turbine blade using fiber Bragg grating sensors

2010 ◽  
Author(s):  
Hyung-joon Bang ◽  
Hyun-ki Shin ◽  
Yung-chul Ju
Keyword(s):  
Structural Health Monitoring ◽  
Wind Turbine ◽  
Fiber Bragg Grating ◽  
Turbine Blade ◽  
Health Monitoring ◽  
Wind Turbine Blade ◽  
Bragg Grating ◽  
Structural Health ◽  
Fiber Bragg Grating Sensors

Prediction of wind turbine blade trailing edge noise under various flow conditions for a passive damage detection system

2021 ◽  
Vol 263 (2) ◽  
pp. 4079-4087
Author(s):  
Murat Inalpolat ◽  
Caleb Traylor
Keyword(s):  
Structural Health Monitoring ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Health Monitoring ◽  
Detection System ◽  
Wind Turbine Blade ◽  
Flow Conditions ◽  
Trailing Edge ◽  
Structural Health ◽  
Acoustic Spectra

Noise generated by turbulent boundary layer over the trailing edge of a wind turbine blade under various flow conditions is predicted and analyzed for structural health monitoring purposes. Wind turbine blade monitoring presents a challenge to wind farm operators, and an in-blade structural health monitoring system would significantly reduce O&M costs. Previous studies into structural health monitoring of blades have demonstrated the feasibility of designing a passive detection system based on monitoring the flow-generated acoustic spectra. A beneficial next step is identifying the robustness of such a system to wind turbine blades under different flow conditions. To examine this, a range of free stream air velocities from 5 m/s to 20 m/s and a range of rotor speeds from 5 rpm to 20 rpm are used in a reduced-order model of the flow-generated sound in the trailing edge turbulent boundary layer. The equivalent lumped acoustics sources are predicted based on the turbulent flow simulations, and acoustic spectra are calculated using acoustic ray tracing. Each case is evaluated based on the changes detected when damage is present. These results can be used to identify wind farms that would most benefit from this monitoring system to increase efficiency in deployment of turbines.

An experimental investigation into passive acoustic damage detection for structural health monitoring of wind turbine blades

2020 ◽  
Vol 19 (6) ◽  
pp. 1711-1725 ◽  
Author(s):  
Jaclyn Solimine ◽  
Christopher Niezrecki ◽  
Murat Inalpolat
Keyword(s):  
Signal Processing ◽  
Structural Health Monitoring ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Health Monitoring ◽  
Fatigue Testing ◽  
Turbine Blades ◽  
Wind Turbine Blade ◽  
Wind Turbine Blades ◽  
Structural Health

This article details the implementation of a novel passive structural health monitoring approach for damage detection in wind turbine blades using airborne sound. The approach utilizes blade-internal microphones to detect trends, shifts, or spikes in the sound pressure level of the blade cavity using a limited network of internally distributed airborne acoustic sensors, naturally occurring passive system excitation, and periodic measurement windows. A test campaign was performed on a utility-scale wind turbine blade undergoing fatigue testing to demonstrate the ability of the method for structural health monitoring applications. The preliminary audio signal processing steps used in the study, which were heavily influenced by those methods commonly utilized in speech-processing applications, are discussed in detail. Principal component analysis and K-means clustering are applied to the feature-space representation of the data set to identify any outliers (synonymous with deviations from the normal operation of the wind turbine blade) in the measurements. The performance of the system is evaluated based on its ability to detect those structural events in the blade that are identified by making manual observations of the measurements. The signal processing methods proposed within the article are shown to be successful in detecting structural and acoustic aberrations experienced by a full-scale wind turbine blade undergoing fatigue testing. Following the assessment of the data, recommendations are given to address the future development of the approach in terms of physical limitations, signal processing techniques, and machine learning options.

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