scholarly journals Towards Visual Inspection of Wind Turbines: A Case of Visual Data Acquisition Using Autonomous Aerial Robots

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 181650-181661
Author(s):  
Christoforos Kanellakis ◽  
Emil Fresk ◽  
Sina Sharif Mansouri ◽  
Dariusz Kominiak ◽  
George Nikolakopoulos
Keyword(s):  
Data Acquisition ◽  
Wind Turbines ◽  
Visual Inspection ◽  
Visual Data ◽  
Aerial Robots

scholarly journals Optimisation of Data Acquisition in Wind Turbines with Data-Driven Conversion Functions for Sensor Measurements

2017 ◽  
Vol 137 ◽  
pp. 571-578 ◽  
Author(s):  
L. Colone ◽  
M. Reder ◽  
J. Tautz-Weinert ◽  
J.J. Melero ◽  
A. Natarajan ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Wind Turbines ◽  
Data Driven

scholarly journals Condition monitoring of wind turbines based on analysis of temperature-related parameters in supervisory control and data acquisition data

2019 ◽  
Vol 53 (1-2) ◽  
pp. 164-180 ◽  
Author(s):  
Xian Wang ◽  
Qiancheng Zhao ◽  
Xuebing Yang ◽  
Bing Zeng
Keyword(s):  
Data Acquisition ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Supervisory Control ◽  
Time Window ◽  
Health Condition ◽  
Health Index ◽  
System Temperature ◽  
Early Health ◽  
Predicted Values

In order to conduct a further in-depth exploration of the role of temperature-related parameters in the condition monitoring of wind turbines, this paper proposes a method to assess the condition of wind turbines by analyzing the supervisory control and data acquisition system temperature-related parameters based on existing research. A prediction model of time-sequence regression is established, based on the key temperature signals of WTs, so as to reflect their health condition in the form of prediction residuals. A kind of health index from the perspective of temperature-related parameters is developed by separating the statistics concerning the conformity of the predicted values of key temperature parameters within a certain time window from the measured values in order to clearly present the implied information on the health condition of wind turbines contained in the model prediction residuals. The case study shows that the trend of health index from the perspective of temperature-related parameters is consistent with the health condition of wind turbines. In some instances, its decline obviously occurs earlier than the maintenance provided to address the stoppage, suggesting that such indexes can effectively reflect some early health problems of the wind turbines to provide a reference for their scientific maintenance.

scholarly journals UAV-Based Oblique Photogrammetry for Outdoor Data Acquisition and Offsite Visual Inspection of Transmission Line

2017 ◽  
Vol 9 (3) ◽  
pp. 278 ◽  
Author(s):  
San Jiang ◽  
Wanshou Jiang ◽  
Wei Huang ◽  
Liang Yang
Keyword(s):  
Transmission Line ◽  
Data Acquisition ◽  
Visual Inspection

scholarly journals Remote data acquisition for condition monitoring of wind turbines

2015 ◽  
Vol 6 (2) ◽  
pp. 10
Author(s):  
Bavo De Maré ◽  
Jacob Sukumaran ◽  
Mia Loccufier ◽  
Patrick De Baets
Keyword(s):  
Data Acquisition ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Good Condition ◽  
Offshore Wind ◽  
Monitoring Systems ◽  
Offshore Wind Turbines ◽  
Condition Monitoring Systems ◽  
Remote Data

While the number of offshore wind turbines is growing and turbines getting bigger and more expensive, the need for good condition monitoring systems is rising. From the research it is clear that failures of the gearbox, and in particular the gearwheels and bearings of the gearbox, have been responsible for the most downtime of a wind turbine. Gearwheels and bearings are being simulated in a multi-sensor environment to observe the wear on the surface.

scholarly journals Mobile LiDAR for Scalable Monitoring of Mechanically Stabilized Earth Walls with Smooth Panels

2020 ◽  
Vol 10 (13) ◽  
pp. 4480
Author(s):  
Abdulla Al-Rawabdeh ◽  
Mohammed Aldosari ◽  
Darcy Bullock ◽  
Ayman Habib
Keyword(s):  
Data Acquisition ◽  
Performance Measures ◽  
Visual Inspection ◽  
Precast Concrete ◽  
Point Clouds ◽  
Mechanically Stabilized Earth ◽  
Qualitative Approaches ◽  
Transportation Corridors ◽  
Mse Walls ◽  
Mechanically Stabilized

Mechanically stabilized earth (MSE) walls rely on its weight to resist the destabilizing earth forces acting at the back of the reinforced soil area. MSE walls are a common infrastructure along national and international transportation corridors as they are low-cost and have easy-to-install precast concrete panels. The usability of such transportation corridors depends on the safety and condition of the MSE wall system. Consequently, MSE walls have to be periodically monitored according to prevailing transportation asset management criteria during the construction and serviceability life stages to ensure that their predictable performance measures are met. To date, MSE walls are monitored using qualitative approaches such as visual inspection, which provide limited information. Aside from being time-consuming, visual inspection is susceptible to bias due to human subjectivity. Manual and visual inspection in the field has been traditionally based on the use of a total station, geotechnical field instrumentation, and/or static terrestrial laser scanning (TLS). These instruments can provide highly accurate and reliable performance measures; however, their underlying data acquisition and processing strategies are time-consuming and not scalable. The proposed strategy in this research provides several global and local serviceability measures through efficient processing of point cloud data acquired by a mobile LiDAR system (MLS) for MSE walls with smooth panels without the need for installing any targets. An ultra-high-accuracy vehicle-based LiDAR data acquisition system has been used for the data acquisition. To check the viability of the proposed methodology, a case study has been conducted to evaluate the similarity of the derived serviceability measures from TLS and MLS technologies. The results of that comparison verified that the MLS-based serviceability measures are within 1 cm and 0.3° of those obtained using TLS and thus confirmed the potential for using MLS to efficiently acquire point clouds while facilitating economical, scalable, and reliable monitoring of MSE walls.

scholarly journals Identification of wind turbine main-shaft torsional loads from high-frequency SCADA (supervisory control and data acquisition) measurements using an inverse-problem approach

2021 ◽  
Vol 6 (6) ◽  
pp. 1401-1412
Author(s):  
W. Dheelibun Remigius ◽  
Anand Natarajan
Keyword(s):  
Mathematical Model ◽  
Inverse Problem ◽  
Data Acquisition ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Supervisory Control ◽  
High Frequency ◽  
Main Shaft ◽  
Site Specific ◽  
Torsional Loads

Abstract. To assess the structural health and remaining useful life of wind turbines within wind farms, the site-specific structural response and modal parameters of the primary structures are required. In this regard, a novel inverse-problem-based methodology is proposed here to identify the dynamic quantities of the drivetrain main shaft, i.e. torsional displacement and coupled stiffness. As a model-based approach, an inverse problem of a mathematical model concerning the coupled-shaft torsional dynamics with high-frequency SCADA (supervisory control and data acquisition) measurements as input is solved. It involves Tikhonov regularisation to minimise the measurement noise and irregularities on the shaft torsional displacement obtained from measured rotor and generator speed. Subsequently, the regularised torsional displacement along with necessary SCADA measurements is used as an input to the mathematical model, and a model-based system identification method called the collage method is employed to estimate the coupled torsional stiffness. It is also demonstrated that the estimated shaft torsional displacement and coupled stiffness can be used to identify the site-specific main-shaft torsional loads. It is shown that the torsional loads estimated by the proposed methodology is in good agreement with the aeroelastic simulations of the Vestas V52 wind turbine. Upon successful verification, the proposed methodology is applied to the V52 turbine to identify the site-specific main-shaft torsional loads and damage-equivalent load. Since the proposed methodology does not require a design basis or additional measurement sensors, it can be directly applied to wind turbines within a wind farm that possess high-frequency SCADA measurements.

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