scholarly journals Three-Tier Modular Structural Health Monitoring Framework Using Environmental and Operational Condition Clustering for Data Normalization: Validation on an Operational Wind Turbine System

2016 ◽  
Vol 104 (8) ◽  
pp. 1632-1646 ◽  
Author(s):  
Moritz W. Hackell ◽  
Raimund Rolfes ◽  
Michael B. Kane ◽  
Jerome P. Lynch
Keyword(s):  
Structural Health Monitoring ◽  
Wind Turbine ◽  
Health Monitoring ◽  
Data Normalization ◽  
Structural Health ◽  
Wind Turbine System ◽  
Monitoring Framework

Vibration-based structural health monitoring of a wind turbine system. Part I: Resonance phenomenon

2015 ◽  
Vol 89 ◽  
pp. 260-272 ◽  
Author(s):  
Wei-Hua Hu ◽  
Sebastian Thöns ◽  
Rolf Günter Rohrmann ◽  
Samir Said ◽  
Werner Rücker
Keyword(s):  
Structural Health Monitoring ◽  
Wind Turbine ◽  
Health Monitoring ◽  
Resonance Phenomenon ◽  
Structural Health ◽  
Wind Turbine System ◽  
System Part

scholarly journals Combination of damage feature decisions with adaptive boosting for improving the detection performance of a structural health monitoring framework: Validation on an operating wind turbine

2020 ◽  
pp. 147592172090937
Author(s):  
Stavroula Tsiapoki ◽  
Omid Bahrami ◽  
Moritz W Häckell ◽  
Jerome P Lynch ◽  
Raimund Rolfes
Keyword(s):  
Structural Health Monitoring ◽  
Hypothesis Testing ◽  
Wind Turbine ◽  
Health Monitoring ◽  
Adaptive Boosting ◽  
Operational Conditions ◽  
Structural Health ◽  
Monitoring Framework ◽  
Adaboost Classifier ◽  
Parameter Values

This article proposes the deployment of adaptive boosting (AdaBoost) for combining damage feature decisions and improving the detection accuracy of structural health monitoring algorithms. In structural health monitoring applications, damage-sensitive features are combined with classifiers to define decision boundaries and provide information about the structural state. Boosting algorithms combine multiple classifiers aiming at the improvement of their performance. In this study, AdaBoost is deployed on the realizations of a modular structural health monitoring framework, which consists of three tiers: data normalization based on environmental and operational conditions; extraction of damage features, also referred to as condition parameters; and hypothesis testing. Each condition parameter–hypothesis testing pair composes a classifier which is used in AdaBoost as a weak classifier. The integration of AdaBoost with the structural health monitoring framework is validated using experimental data of a 3-kW wind turbine located at the Los Alamos National Laboratory and data generated from a mechanical model of the same structure. The AdaBoost classifier is evaluated with respect to the error rate as well as the true positive and false positive rates, which are typically used in receiver operating characteristic curves. The AdaBoost classifier outperforms the framework classifiers in many cases, improving drastically the detection performance. However, it is shown that the boosting performance depends on the relative location of the condition parameter values on the condition parameter space. The overlaps between the condition parameter values to be combined are quantified using the Bhattacharyya coefficient, which provides a metric for assessing the boosting potential. Finally, omitting condition parameter values corresponding to specific environmental and operational conditions from the boosting process is proposed for obtaining optimum boosting results.

Structural Health Monitoring of Research-Scale Wind Turbine Blades

2013 ◽  
Vol 558 ◽  
pp. 364-373 ◽  
Author(s):  
Stuart G. Taylor ◽  
Kevin M. Farinholt ◽  
Gyu Hae Park ◽  
Charles R. Farrar ◽  
Michael D. Todd ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Embedded Systems ◽  
Wind Turbine ◽  
Health Monitoring ◽  
Turbine Blades ◽  
Field Tests ◽  
Ultrasonic Waves ◽  
Wind Turbine Blades ◽  
Structural Health ◽  
Sensing System

This paper presents ongoing work by the authors to implement real-time structural health monitoring (SHM) systems for operational research-scale wind turbine blades. The authors have been investigating and assessing the performance of several techniques for SHM of wind turbine blades using piezoelectric active sensors. Following a series of laboratory vibration and fatigue tests, these techniques are being implemented using embedded systems developed by the authors. These embedded systems are being deployed on operating wind turbine platforms, including a 20-meter rotor diameter turbine, located in Bushland, TX, and a 4.5-meter rotor diameter turbine, located in Los Alamos, NM. The SHM approach includes measurements over multiple frequency ranges, in which diffuse ultrasonic waves are excited and recorded using an active sensing system, and the blades global ambient vibration response is recorded using a passive sensing system. These dual measurement types provide a means of correlating the effect of potential damage to changes in the global structural behavior of the blade. In order to provide a backdrop for the sensors and systems currently installed in the field, recent damage detection results for laboratory-based wind turbine blade experiments are reviewed. Our recent and ongoing experimental platforms for field tests are described, and experimental results from these field tests are presented. LA-UR-12-24691.

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.

Radar-based structural health monitoring of wind turbine blades: The case of damage detection

2017 ◽  
Vol 17 (4) ◽  
pp. 815-822 ◽  
Author(s):  
Jochen Moll ◽  
Philip Arnold ◽  
Moritz Mälzer ◽  
Viktor Krozer ◽  
Dimitry Pozdniakov ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Wind Turbine ◽  
Health Monitoring ◽  
Turbine Blades ◽  
Heterogeneous Material ◽  
Material System ◽  
Wind Turbine Blades ◽  
Millimetre Wave ◽  
Structural Health

Structural health monitoring of wind turbine blades is challenging due to its large dimensions, as well as the complex and heterogeneous material system. In this article, we will introduce a radically new structural health monitoring approach that uses permanently installed radar sensors in the microwave and millimetre-wave frequency range for remote and in-service inspection of wind turbine blades. The radar sensor is placed at the tower of the wind turbine and irradiates the electromagnetic waves in the direction of the rotating blades. Experimental results for damage detection of complex structures will be presented in a laboratory environment for the case of a 10-mm-thick glass-fibre-reinforced plastic plate, as well as a real blade-tip sample.

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