Digital Image Correlation Techniques for NDE and SHM

Niezrecki, Christopher; Baqersad, Javad; Sabato, Alessandro

doi:10.1007/978-3-319-26553-7_47

Cited by 13 publications

(9 citation statements)

References 153 publications

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“…Facets displacement vector d showing a simultaneous displacement and rotation (source: Niezrecki et al 29 ) [Colour figure can be viewed at wileyonlinelibrary.com]…”

Section: Background Of Optical Sensing Thechniquesmentioning

confidence: 99%

“…Advancements in camera technology and image‐processing algorithms have made computer vision techniques a viable option for streamlining the blade testing protocol. Three‐dimensional (3D) digital image correlation (DIC) and 3D point tracking (3DPT) systems have proven their efficiency in being able to perform noncontact inspections 29 . They provide accurate quantitative information of structural deformations, full‐field strain maps, geometry profiles, and modal parameters 30 over a wide range of civil and mechanical engineering systems such as rotating machinery, 31 bridges, 32 railroad tracks, 33 and composite materials 34 .…”

Section: Introductionmentioning

confidence: 99%

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Multicamera measurement system to evaluate the dynamic response of utility‐scale wind turbine blades

et al. 2020

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Wind turbine blade certification requires static and fatigue testing at a large-scale facility similar to the Wind Technology Testing Center (WTTC) located in Charlestown, Massachusetts. Usually, these tests are conducted by using wire-based sensors such as strain gages, accelerometers, and string potentiometers. These systems are expensive, require a time-consuming installation (e.g., up to 3 weeks and $35 k-$50 k for a strain gage system on a 55-m-long blade), are difficult to deploy on large-sized structures, require additional instrumentations (e.g., power amplifiers and data acquisition systems), and produce results only at a handful of a discrete number of measurement points. In this study, a multicamera measurement system is implemented and experimentally evaluated to obtain full-field displacement and strain over a~12-m-long portion of a~60-m utility-scale wind turbine blade. The proposed system has the potential to streamline the certification process by reducing the blade's preparation and sensor installation cost and time to a few hundreds of dollars (for painting equipment) and a few days for preparing the surface of the blade for the test. Furthermore, operational modal analysis was used in conjunction with the multicamera system to estimate the natural frequencies and mode shapes of the wind turbine blade. The obtained results have shown that the proposed approach can detect in-plane displacement as low as 0.2 mm, mechanical strain with an error below 3% when compared with measurement performed using strain gages, and the first five natural frequencies with an error below 2% when compared with data recorded using traditional wire-based accelerometers. This paper presents these results and provides a summary of the strengths and weaknesses of the proposed optical measurement approach in the context of streamlining the blade certification/testing process and performing vision-based structural dynamic measurements on large-scale structures. K E Y W O R D Sdigital image correlation, fatigue tests, operational modal analysis, stitched field of view, structural health monitoring, wind turbine blades

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“…Facets displacement vector d showing a simultaneous displacement and rotation (source: Niezrecki et al 29 ) [Colour figure can be viewed at wileyonlinelibrary.com]…”

Section: Background Of Optical Sensing Thechniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multicamera measurement system to evaluate the dynamic response of utility‐scale wind turbine blades

et al. 2020

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“…2021, 11, x FOR PEER REVIEW 3 of 20 pixel intensity map and significantly simplifies the applied classical to advanced matching algorithms procedure, if the object is illuminated adequately [18,[38][39][40][41][42]. Matching principals from the classical correlation coefficient [1,10,[43][44][45][46], intensity interpolation [47][48][49][50], Newton-Raphson method [51][52][53], gradient-based method [54][55][56][57], and genetic algorithm [58][59][60] to the advanced artificial and convolutional neural network [61][62][63][64][65][66] requires a high dynamic range of grayscale values, sharp edges, and high contrast images. These approaches can be challenging to implement because automatic and robust measurement identification and matching at either the pixel or the sub-pixel level is difficult for large image data captured under a low-light setting.…”

Section: Methodsmentioning

confidence: 99%

Underexposed Vision-Based Sensors’ Image Enhancement for Feature Identification in Close-Range Photogrammetry and Structural Health Monitoring

Ngeljaratan

Moustafa

2021

Applied Sciences

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This paper describes an alternative structural health monitoring (SHM) framework for low-light settings or dark environments using underexposed images from vision-based sensors based on the practical implementation of image enhancement algorithms. The proposed framework was validated by two experimental works monitored by two vision systems under ambient lights without assistance from additional lightings. The first experiment monitored six artificial templates attached to a sliding bar that was displaced by a standard one-inch steel block. The effect of image enhancement in the feature identification and bundle adjustment integrated into the close-range photogrammetry were evaluated. The second validation was from a seismic shake table test of a full-scale three-story building tested at E-Defense in Japan. Overall, this study demonstrated the efficiency and robustness of the proposed image enhancement framework in (i) modifying the original image characteristics so the feature identification algorithm is capable of accurately detecting, locating and registering the existing features on the object; (ii) integrating the identified features into the automatic bundle adjustment in the close-range photogrammetry process; and (iii) assessing the measurement of identified features in static and dynamic SHM, and in structural system identification, with high accuracy.

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“…In the field of structural health monitoring, many non-contact and nondestructive inspection techniques have been adopted to inspect structural integrity, including visual inspection, shearography or speckle pattern shearing interferometry, ultrasonic inspection, vibration analysis, radiography, thermography, acoustic emission (AE), and digital image correlation (DIC) (Ida and Meyendorf, 2019; Niezrecki et al, 2019). AE is generated by acoustic waves which are produced from irreversible changes such as cracks or dislocations within the material (Grosse and Ohtsu, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…Recently, non-contact optical methods for rapid inspection over the full structure have received more attention due to their high spatial resolution, high precision, and lower instrumentation costs. Of these various “full-field” methods, DIC (Baqersad et al, 2017; Goidescu et al, 2013) and three-dimensional point tracking (Baqersad et al, 2015a) have both recently been studied for the health monitoring of wind turbine blades (Niezrecki et al, 2019). Recent studies show that 3D DIC can effectively monitor the integrity of the civil infrastructure (Cigada et al, 2013) or biological systems (Mostafavi Yazdi and Baqersad, 2022; Panchal et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Nondestructive evaluation of carbon-fiber composites using digital image correlation, acoustic emission, and optical based modal analysis

et al. 2022

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Composite materials are increasingly used in the wind industries. Damage detection and health monitoring of composite materials are challenging due to the complex internal structure and unique material properties. Digital image correlation (DIC) and acoustic emission (AE) are both used for damage detection in structures. In this work, DIC performs a full-field strain measurement on the surface of the carbon-fiber specimen while AE continuously monitors and records the AE signals generated from specimen subsurface structure failures. These health monitoring techniques are integrated and evaluated in this study to correlate surface strain measurements and acoustic emission measurements on carbon-fiber specimens. The AE measurement results show that there is a correlation between the occurrence of AE events and the timing of complete specimen failure. DIC with a high-speed stereo camera system is also adopted to extract the change in the resonance frequencies and displacement and strain mode shapes of the specimen during experiments in cyclic loading.

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Digital Image Correlation Techniques for NDE and SHM

Cited by 13 publications

References 153 publications

Multicamera measurement system to evaluate the dynamic response of utility‐scale wind turbine blades

Multicamera measurement system to evaluate the dynamic response of utility‐scale wind turbine blades

Underexposed Vision-Based Sensors’ Image Enhancement for Feature Identification in Close-Range Photogrammetry and Structural Health Monitoring

Nondestructive evaluation of carbon-fiber composites using digital image correlation, acoustic emission, and optical based modal analysis

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