scholarly journals Dynamic Wind Turbine Blade Inspection Using Micro-Polarisation Spatial Phase Shift Digital Shearography

2021 ◽  
Vol 11 (22) ◽  
pp. 10700
Author(s):  
Zhiyao Li ◽  
Mohammad Osman Tokhi ◽  
Ryan Marks ◽  
Haitao Zheng ◽  
Zhanfang Zhao
Keyword(s):  
Phase Shift ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Thermal Loading ◽  
Wind Turbine Blade ◽  
Phase Shifting ◽  
Spatial Phase ◽  
Fourier Filtering ◽  
Fringe Pattern Analysis ◽  
Spatial Phase Shift

Shearography, as a novel non-destructive evaluation technique, has shown notable ability in the detection of composite materials. However, in current shearography practices, the phase shifting and loading methods applied are mainly static. For instance, vacuum hood or force loading facilities are often used in phase-shifting shearography, and these are hard to realise with robotic control, especially for on-board inspection. In this study, a dynamic process for detecting defects in the subsurface of a wind turbine blade (WTB) using spatial phase shift with dynamic thermal loading was proposed. The WTB sample underwent a dynamic thermal loading operation, and its status is captured by a Michelson interferometric-based spatial phase shift digital shearography system using a pixelated micro-polarisation array sensor. The captured images were analysed in a 2D frequency domain and low-pass filtered for phase map acquisition. The initial phase maps underwent a window Fourier filtering process and were integrated to produce a video sequence for realisation of visualising the first derivative of the displacement in the process of thermal loading. The approach was tested in experimental settings and the results obtained were presented and discussed. A comparative assessment of the approach with shearography fringe pattern analysis and temporal phase shift technique is also presented and discussed.

Perception of Subjective Contours in Fish

Perception ◽  
10.1068/p6121 ◽  
2009 ◽  
Vol 38 (4) ◽  
pp. 579-590 ◽  
Author(s):  
Valeria Anna Sovrano ◽  
Angelo Bisazza
Keyword(s):  
Phase Shift ◽  
Luminance Contrast ◽  
Phase Shifting ◽  
Orientation Discrimination ◽  
Illusory Contours ◽  
Geometric Figure ◽  
Spatial Phase ◽  
Spatial Phase Shift ◽  
Subjective Surfaces

The ability of fish to perceive subjective (or illusory) contours, ie contours that lack a physical counterpart in terms of luminance contrast gradients, was investigated. In the first experiment, redtail splitfins ( Xenotoca eiseni), family Goodeidae, were trained to discriminate between a geometric figure (a triangle or a square) on various backgrounds and a background without any figure. Thereafter, the fish performed test trials in which illusory squares or triangles were obtained by (i) interruptions of a background of diagonal lines, (ii) phase-shifting of a background of diagonal lines, and (iii) pacmen spatially arranged to induce perception of Kanizsa subjective surfaces. In all three conditions, fish seemed to generalise their responses to stimuli perceived as subjective contours by humans. Fish chose, correctly, squares or triangles made of interrupted or phase-shifted diagonal lines from uniform backgrounds of diagonal lines, as well as illusory square or triangle Kanizsa figures from figures in which the inducing pacmen were scrambled. In the second experiment, fish were trained to discriminate between a vertical and a horizontal bar with luminance contrast gradients, and then tested with vertically and horizontally oriented illusory bars, created either through interruption or spatial phase-shift of inducing diagonal lines. Fish appeared to be able to generalise the orientation discrimination to illusory contours. These results demonstrate that redtail splitfins are capable of perceiving illusory contours.

Noise analysis of the wind turbine blade

2013 ◽  
Author(s):  
Gwochung Tsai ◽  
Yita Wang ◽  
Yuhchung Hu ◽  
Jaching Jiang
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Noise Analysis ◽  
Wind Turbine Blade

DYNAMIC ANALYSIS OF THE FLUID FLOW OVER A WIND TURBINE BLADE

2017 ◽  
Author(s):  
Aldemir Ap Cavalini Jr ◽  
João Marcelo Vedovoto ◽  
Renata Rocha
Keyword(s):  
Fluid Flow ◽  
Dynamic Analysis ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Wind Turbine Blade

scholarly journals Image Enhanced Mask R-CNN: A Deep Learning Pipeline with New Evaluation Measures for Wind Turbine Blade Defect Detection and Classification

2021 ◽  
Vol 7 (3) ◽  
pp. 46
Author(s):  
Jiajun Zhang ◽  
Georgina Cosma ◽  
Jason Watkins
Keyword(s):  
Deep Learning ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Defect Detection ◽  
Recognition Rate ◽  
Weighted Average ◽  
Wind Turbine Blade ◽  
Evaluation Measures ◽  
Augmentation Techniques ◽  
The Mean

Demand for wind power has grown, and this has increased wind turbine blade (WTB) inspections and defect repairs. This paper empirically investigates the performance of state-of-the-art deep learning algorithms, namely, YOLOv3, YOLOv4, and Mask R-CNN for detecting and classifying defects by type. The paper proposes new performance evaluation measures suitable for defect detection tasks, and these are: Prediction Box Accuracy, Recognition Rate, and False Label Rate. Experiments were carried out using a dataset, provided by the industrial partner, that contains images from WTB inspections. Three variations of the dataset were constructed using different image augmentation settings. Results of the experiments revealed that on average, across all proposed evaluation measures, Mask R-CNN outperformed all other algorithms when transformation-based augmentations (i.e., rotation and flipping) were applied. In particular, when using the best dataset, the mean Weighted Average (mWA) values (i.e., mWA is the average of the proposed measures) achieved were: Mask R-CNN: 86.74%, YOLOv3: 70.08%, and YOLOv4: 78.28%. The paper also proposes a new defect detection pipeline, called Image Enhanced Mask R-CNN (IE Mask R-CNN), that includes the best combination of image enhancement and augmentation techniques for pre-processing the dataset, and a Mask R-CNN model tuned for the task of WTB defect detection and classification.

Impact of dielectric barrier discharge plasma on the wake of a wind turbine blade section oscillating in plunge

2021 ◽  
pp. 095765092110337
Author(s):  
GH Maleki ◽  
Ali R Davari ◽  
MR Soltani
Keyword(s):  
Wind Turbine ◽  
Dielectric Barrier Discharge ◽  
Turbine Blade ◽  
Discharge Plasma ◽  
Barrier Discharge ◽  
Wind Turbine Blade ◽  
Plasma Actuators ◽  
Dielectric Barrier Discharge Plasma ◽  
Dielectric Barrier ◽  
Barrier Discharge Plasma

Effects of dielectric barrier discharge plasma have been studied on the wake velocity profiles of a section of a 660 kW wind turbine blade in plunging motion in a wind tunnel. The corresponding unsteady velocity profiles show remarkable improvement when the plasma actuators were operating and the angles of attack of the model were beyond the static stall angles of the airfoil. As a result the drag force was considerably reduced. It is further observed that the plasma-induced flow attenuates the leading edge vortices that are periodically shed into wake and diminishes the large eddies downstream. The favorable effects of the plasma augmentation are shown to occur near the uppermost and lowermost positions of the plunging paths where the wake is primarily dominated by the vortices of the same sign. The wake structure in the presence of the flow induced by the plasma actuators shows that the actual effective angles of attack seen by the plunging airfoil reduces in comparison with that for the case of the plasma augmentation off situation.

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