Approach to weld segmentation and defect classification in radiographic images of pipe welds

Golodov, V.A.; Maltseva, A

doi:10.1016/j.ndteint.2021.102597

Cited by 25 publications

(12 citation statements)

References 13 publications

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“…As a result, it is possible to discover which equipment is affected, the implications of these problems on the company's productivity and the best maintenance plan to minimize the recurrence of these failures. Therefore, companies need to optimize it as much as possible to reduce this type of risk and losses [24].…”

Section: Document Searchmentioning

confidence: 99%

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A Scoping Review of Pipeline Maintenance Methodologies Based on Industry 4.0

et al. 2022

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The fourth industrial revolution was a milestone at the industrial level. It forced most industries to evolve technically and for their collaborators to prepare and advance together with technology; the oil industry was no exception. It develops its activities in dangerous and dynamic environments and needs to protect its human resources, equipment, and infrastructure. This article presents a scoping review, based on the PRISMA guidelines, of pipeline maintenance methodologies based on Industry 4.0. From the first collection of 123 articles from prestigious databases such as SpringerLink, MDPI, Scopus, IEEEXplore and ACM, a final sample of 31 articles was obtained. Here, technologies that enhance preventive and predictive maintenance systems are discussed. The results show that predictive maintenance compared to preventive maintenance has a percentage difference in upkeep time optimization of 38% in the last five years. This difference was corroborated with a Student’s t-test for independent samples, with a significance of 0.023. Likewise, the most used technologies were analyzed, with artificial intelligence standing out with 45.16%.

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Section: Document Searchmentioning

confidence: 99%

“…To solve these drawbacks, Ref. [24] proposes the application of machine learning for the real-time processing of welding radiographs. The digital detector array method was employed.…”

Section: Literature Reviewmentioning

confidence: 99%

A Scoping Review of Pipeline Maintenance Methodologies Based on Industry 4.0

et al. 2022

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“…The process of identifying defects has the potential to experience identification errors because it is very much determined by the subjectivity of AR. Therefore, we need a system that can help AR work in identifying weld discontinuities on digital radiographic images automatically [3].…”

Section: Introductionmentioning

confidence: 99%

Development of welding discontinuity identification system using statistical texture feature extraction and ANN classification on digital radiographic image

Ahmadi

Arifian²,

Mulyono³

et al. 2023

Armatur

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Discontinuity in welds is one of the causes of the quality of a connection in the material decreases function. Undamaged test with radiographic method is one of the tests to see the quality of a weld. The test results are radiograph images and evaluated by a radiographer. So this research is designed by optimizing a system to help the work of a radiography expert in identifying discontinuities by utilizing the Matlab Application. On this system uses the method of characteristic extraction and classification of neural networks (AAN). The system uses a characteristic extraction method with geometric invariant moment (GIM) algorithms and a gray level co-occurenece matrix (GLCM) as identification values used in the classification process. The calcification process uses a backpropagation-type multilayer Artificial Neural Network. The types of discontinuities used as data in this system are incompleted of penetration, crack, wormhole, and distributed porosity using a total of 800 datasets of radiograph imagery data. This data sharing is organized using k fold cross validation. The study conducted 15 experiments in system testing to prove the truth in identifying. The results of the experiment resulted in the highest average performance score reaching 93.33%

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“…Jin et al [11]used U-Net for welding seam cutting and achieved an mIOU of 85.44% on the test set. Golodov et al [12]used the FgSegNet_v2 model for the welding seam area extraction task and also achieved good recognition results.…”

Section: Introductionmentioning

confidence: 99%

Weld region salient object extraction based on MC-U2Net

Wang

2023

Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023)

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The intelligent reading of industrial radiographic film defect information for welds has always been an important issue, usually divided into three steps: image pre-processing, feature extraction, and defect recognition. In the image preprocessing process, it is necessary to extract the weld area, reduce the number of parameters, and avoid interference from the base material area and lead identification on defect recognition. However, on most digital films after grayscale transformation, there is usually a grayscale gradient from white to grey to black from the weld to the base material, which causes the weld boundary to be blurred and the weld area cannot be directly extracted. In this article, we divided the digital films into three regions, "welding seam region", "uncertain region", and "non-welding seam region", based on the grey value changes, and assigned labels "correct", "unknown", and "wrong" to them respectively. We trained these three regions using the U Net, and the regions predicted as "unknown" and "correct" were combined as welding seam region. The experimental results showed that the proposed method achieved a reasonable prediction rate (RP) of 95.45% on the test set, with a minimum MAE index of 0.0028.

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Approach to weld segmentation and defect classification in radiographic images of pipe welds

Cited by 25 publications

References 13 publications

A Scoping Review of Pipeline Maintenance Methodologies Based on Industry 4.0

A Scoping Review of Pipeline Maintenance Methodologies Based on Industry 4.0

Development of welding discontinuity identification system using statistical texture feature extraction and ANN classification on digital radiographic image

Weld region salient object extraction based on MC-U2Net

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