Weld defect intelligent identification for oil and gas pipelines based on the deep learning models

被引：0

作者：

Luo, Renze ^{[1
,2
,3
]}

Wang, Lei ^{[1
]}

机构：

[1] School of Computer Science and Software Engineering, Southwest Petroleum University, Sichuan, Chengdu

[2] State Key laboratory of Oil & Gas Reservoir Geology and Exploitation/, Southwest Petroleum University, Sichuan, Chengdu

[3] School of Electrical Engineering and Information, Southwest Petroleum University, Sichuan, Chengdu

来源：

Natural Gas Industry | 2024年 / 44卷 / 09期

关键词：

Attention mechanism; Deep learning; Defect intelligent identification; Image processing; Oil and gas pipeline weld;

D O I：

10.3787/j.issn.1000-0976.2024.09.018

中图分类号：

学科分类号：

摘要：

Welding technology is widely used in the connection of oil and gas pipelines, so ensuring the reliability of weld areas is crucial for the safe operation of oil and gas pipelines. Due to the limitation of process and technology, different types of weld defects may occur in the process of oil and gas pipeline welding. To address the problems such as small difference between defect and background leading to poor defect identification result and large manual identification workload, this paper proposes a novel method of intelligent identification of pipeline weld defects based on the SCT-ResNet50 model. This newly proposed method involves firstly inputting weld area images into a feature extraction network, then applying the SCC (Spatial Channel Context) in the shallow layers of feature extraction to perform local spatial and channel information fusion and the ECA-MHSA in the deep layers to capture long-range dependencies and contextual information, and finally obtaining the ultimate defect identification results through fully connected layers and Softmax. The following research results are obtained. First, the novel method achieves a defect identification accuracy of 98.28% on a data set of weld defects from X-ray images of oil and gas pipelines. Second, compared with the classification methods such as ResNet50, VGG16, DenseNet121, MobileNetv3 and EfficientNetv2, its accuracy is 3.05%, 46.05%, 28.99%, 15.95% and 18.84% higher, respectively. Third, in the scenarios with small sizes of pipeline weld defects and small differences between defects and backgrounds, this novel method exhibits a higher accuracy in identifying weld defects in oil and gas pipelines. In conclusion, the advantage of the algorithm of this novel method lies in the combination of SCC module with the local information and global information of ECA-MHSA module learning image. This novel method effectively improves the classification of weld defects in oil and gas pipelines, providing technical support for the safe operation of oil and gas pipelines. © 2024 Natural Gas Industry Journal Agency. All rights reserved.

引用

页码：199 / 208

页数：9

共 40 条

[21]

XU Juefei, BODDETI V N, SAVVIDES M., Local binary convolutional neural networks, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2017)

[22]

FUKUSHIMA K., Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position, Biological Cybernetics, 36, 4, pp. 193-202, (1980)

[23]

LIU X Y, LIU J H, QU F M, Et al., A weld defect detection method based on triplet deep neural network, Chinese Control and Decision Conference, pp. 644-648, (2020)

[24]

MIAO R, JIANG Z, ZHOU Q, Et al., Online inspection of narrow overlap weld quality using two-stage convolution neural network image recognition, Machine Vision and Applications, 32, pp. 1-14, (2021)

[25]

AJMI C, ZAPATA J, ELFERCHICHI S, Et al., Deep learning technology for weld defects classification based on transfer learning and activation features, Advances in Materials Science and Engineering, 2020, pp. 1-16, (2020)

[26]

NAZAROV R M, GIZATULLIN Z M, KONSTANTINOV E S., Classification of defects in welds using a convolution neural network, 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus), (2021)

[27]

HOU W H, RAO L K, ZHU A D, Et al., Feature fusion for weld defect classification with small dataset, Journal of Sensors, pp. 1-9, (2022)

[28]

LIU Xiaobao, ZHANG Jiaxiang, YIN Yanchao, Et al., Aluminum profile surface defect detection driven by master-slave feature fusion, Computer Integrated Manufacturing Systems, 29, 1, pp. 192-199, (2023)

[29]

BACIOIU D, MELTON G, PAPAELIAS M, Et al., Automated defect classification of Aluminium 5083 TIG welding using HDR camera and neural networks, Journal of Manufacturing Processes, 45, pp. 603-613, (2019)

[30]

SAY D, ZIDI S, QAISAR S M, Et al., Automated categorization of multiclass welding defects using the X-ray image augmentation and convolutional neural network, Sensors, 23, 14, (2023)

← 1 2 3 4 →