Image-based structural health monitoring of research-scale wind turbine blade

2014 ◽  
pp. 1057-1063
Author(s):  
C Loh ◽  
Y Huang ◽  
W Hsiung ◽  
Y Yang
Keyword(s):  
Structural Health Monitoring ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Health Monitoring ◽  
Wind Turbine Blade ◽  
Structural Health

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.

scholarly journals Lightning strike evaluation on composite and biocomposite vertical-axis wind turbine blade using structural health monitoring approach

2018 ◽  
Vol 29 (17) ◽  
pp. 3444-3455 ◽  
Author(s):  
Siti Zubaidah Mat Daud ◽  
Faizal Mustapha ◽  
Zuraimy Adzis
Keyword(s):  
Structural Health Monitoring ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Health Monitoring ◽  
Turbine Blades ◽  
Flax Fibre ◽  
Wind Turbine Blade ◽  
Lightning Strike ◽  
Wind Turbine Blades ◽  
Structural Health

Lightning damages to wind turbine blades increase as the size of wind turbine becomes larger. The damages are quite serious since it contributes to high cost of repairs and loss of electric power production. Since natural plant fibres receive attention nowadays due to low density and high specific strength, the usage of natural fibre to replace synthetic fibre in fabricating the turbine blade will promote a green renewable material. As the application of biocomposite for wind turbine blade has been explored, the effect of lightning strike damage has been required for further test investigation. This article explores the comparative results of performing lightning strike damage testing on composite (fibreglass) and biocomposite (flax fibre) wind turbine blades with protective metal mesh materials. The damage assessments were performed using visual non-destructive inspection methods and piezoelectric sensor as part of structural health monitoring. From the result, it is found that fibreglass blade experienced larger damage area compared to flax fibre blade based on the visual test conducted.

scholarly journals Inspection and monitoring of wind turbine blade-embedded wave defects during fatigue testing

2014 ◽  
Vol 13 (6) ◽  
pp. 629-643 ◽  
Author(s):  
Christopher Niezrecki ◽  
Peter Avitabile ◽  
Julie Chen ◽  
James Sherwood ◽  
Troy Lundstrom ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Health Monitoring ◽  
Fatigue Testing ◽  
Load Level ◽  
Wind Turbine Blade ◽  
Image Correlation ◽  
Strain Sensing ◽  
Structural Health

The research presented in this article focuses on a 9-m CX-100 wind turbine blade, designed by a team led by Sandia National Laboratories and manufactured by TPI Composites Inc. The key difference between the 9-m blade and baseline CX-100 blades is that this blade contains fabric wave defects of controlled geometry inserted at specified locations along the blade length. The defect blade was tested at the National Wind Technology Center at the National Renewable Energy Laboratory using a schedule of cycles at increasing load level until failure was detected. Researchers used digital image correlation, shearography, acoustic emission, fiber-optic strain sensing, thermal imaging, and piezoelectric sensing as structural health monitoring techniques. This article provides a comparison of the sensing results of these different structural health monitoring approaches to detect the defects and track the resultant damage from the initial fatigue cycle to final failure.

Passive Structural Health Monitoring Applied to a Wind Turbine Model

2009 ◽  
Author(s):  
Kyle Bassett ◽  
Rupp Carriveau ◽  
David S.-K. Ting
Keyword(s):  
Structural Health Monitoring ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Health Monitoring ◽  
Large Scale ◽  
Residual Error ◽  
Series Data ◽  
Vibration Signals ◽  
Structural Health ◽  
Monitoring Technique

Structural health monitoring is a technique devised to monitor the structural conditions of a system in an attempt to take corrective measures before the system fails. A passive structural health monitoring technique is presented, which serves to leverage historic time series data in order to both detect and localize damage on a wind turbine blade aerodynamic model. First, vibration signals from the healthy system are recorded for various input conditions. The data is normalized and auto-regressive (AR) coefficients are determined in order to uniquely identify the normal behavior of the system for each input condition. This data is then stored in a healthy state database. When the structural condition of the system is unknown the vibration signals are acquired, normalized and identified by their AR coefficients. Damage is detected through the residual error which is calculated as the difference between the AR coefficients of the unknown and healthy structural conditions. This technique is tailored for wind turbines and the application of this approach is demonstrated in a wind tunnel using a small turbine blade held with four springs to create a dual degree-of-freedom system. The vibration signals from this system are characterized by free-stream speed. Damage is replicated through mass addition on each of the blades ends and is located by an increase in residual error from the accelerometer mounted closest to the damaged area. The outlined procedure and demonstration illustrate a single stage structural health monitoring technique that, when applied on a large scale, can avoid catastrophic turbine disasters and work to effectively reduce the maintenance costs and downtime of wind farm operations.

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