Development of a Robotic System with Stand-Alone Monocular Vision System for Eco-friendly Defect Detection in Oil Transportation Pipelines

Mudugamuwa, Amith; Jayasundara, Chathura; Han, Baokun; Amarasinghe, Y. W. R.

doi:10.1007/978-981-15-8131-1_10

Cited by 4 publications

(2 citation statements)

References 10 publications

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“…The advancements in electronics, manufacturing, and computational ability have enabled vision sensing applicable for miniaturised systems, such as microfluidic devices [27]. Vision systems and algorithms can perform multiplex operations accurately with a fast response time and high sensitivity and acquire detailed data with minimum hardware interactions with other components in a non-contact manner [28]. Digital Image Processing (DIP) techniques can extract single or multiple features using a digital image [29], which is highly beneficial in object identification.…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Performance Analysis of an Active Microfluidic Droplet Generation System Using Droplet Images

Mudugamuwa

Hettiarachchi

Melroy

et al. 2022

Sensors

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This paper discusses an active droplet generation system, and the presented droplet generator successfully performs droplet generation using two fluid phases: continuous phase fluid and dispersed phase fluid. The performance of an active droplet generation system is analysed based on the droplet morphology using vision sensing and digital image processing. The proposed system in the study includes a droplet generator, camera module with image pre-processing and identification algorithm, and controller and control algorithm with a workstation computer. The overall system is able to control, sense, and analyse the generation of droplets. The main controller consists of a microcontroller, motor controller, voltage regulator, and power supply. Among the morphological features of droplets, the diameter is extracted from the images to observe the system performance. The MATLAB-based image processing algorithm consists of image acquisition, image enhancement, droplet identification, feature extraction, and analysis. RGB band filtering, thresholding, and opening are used in image pre-processing. After the image enhancement, droplet identification is performed by tracing the boundary of the droplets. The average droplet diameter varied from ~3.05 mm to ~4.04 mm in the experiments, and the average droplet diameter decrement presented a relationship of a second-order polynomial with the droplet generation time.

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Section: Introductionmentioning

confidence: 99%

Vision-Based Performance Analysis of an Active Microfluidic Droplet Generation System Using Droplet Images

Mudugamuwa

Hettiarachchi

Melroy

et al. 2022

Sensors

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“…Gas and oil pipeline networks need periodic inspections for monitoring defects that can generate damages to the surrounding environment [1,2]. Early detection of these defects could help avoid accidents in a pipeline [3,4].…”

Section: Introductionmentioning

confidence: 99%

Measurements of the Magnetic Field Variations Related with the Size of V-Shaped Notches in Steel Pipes

et al. 2021

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Gas and oil pipeline networks require periodic inspections to detect cracks or notches that can cause industrial accidents and environmental contamination. For these inspections, the metal magnetic memory (MMM) method could be used as a non-destructive testing (NDT) technique, which does not need expensive equipment and high-skilled operators. However, more investigations are required to quantify the size and shape of defects in ferromagnetic pipes using the MMM signals. We present experimental measurements of MMM signals around five small V-shaped notches of an ASTM-A36 steel pipe using a three-axis magnetoresistive sensor. The V-shaped notches have different values of depth (500 µm, 1000 µm, 1500 µm, 2000 µm and 2500 µm) and width (1000 µm, 1500 µm, 2000 µm, 3000 µm and 3500 µm). We measured the variations of tangential and normal MMM signals around these defects and their relationships with the size of each defect. The first V-notch defect (500 μm depth and 1000 μm width) registers variations of the tangential and normal MMM signals of 14.32 μT ± 1.62 μT and 27.95 μT ± 1.14 μT, respectively. On the other hand, the fifth V-notch defect (2500 μm depth and 3500 μm width) has variations of the tangential and normal MMM signals of 68.75 μT ± 1.10 μT and 71.37 μT ± 0.72 μT, respectively. The MMM method could be used for real-time monitoring of V-shaped notches in steel pipes. This method does not require special treatment of steel pipes.

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Design and development of a microfluidic droplet generator with vision sensing for lab-on-a-chip devices

Hettiarachchi

Melroy

Mudugamuwa

et al. 2021

Sensors and Actuators A: Physical

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Development of a Robotic System with Stand-Alone Monocular Vision System for Eco-friendly Defect Detection in Oil Transportation Pipelines

Cited by 4 publications

References 10 publications

Vision-Based Performance Analysis of an Active Microfluidic Droplet Generation System Using Droplet Images

Vision-Based Performance Analysis of an Active Microfluidic Droplet Generation System Using Droplet Images

Measurements of the Magnetic Field Variations Related with the Size of V-Shaped Notches in Steel Pipes

Design and development of a microfluidic droplet generator with vision sensing for lab-on-a-chip devices

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