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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Deep Learning for Magnetic Flux Leakage Detection and Evaluation of Oil & Gas Pipelines: A Review
Songling Huang, Lisha Peng, Hongyu Sun, et al.
Energies (2023) Vol. 16, Iss. 3, pp. 1372-1372
Open Access | Times Cited: 23

Showing 23 citing articles:

Magnetic particle inspection: Status, advances, and challenges — Demands for automatic non-destructive testing
Qiang Wu, Kang Dong, Xunpeng Qin, et al.
NDT & E International (2023) Vol. 143, pp. 103030-103030
Closed Access | Times Cited: 20
Systematic Evaluation of Ultrasonic In-Line Inspection Techniques for Oil and Gas Pipeline Defects Based on Bibliometric Analysis
Jie Huang, Pengchao Chen, Rui Li, et al.
Sensors (2024) Vol. 24, Iss. 9, pp. 2699-2699
Open Access | Times Cited: 6
Improving Pipeline Magnetic Flux Leakage (MFL) Detection Performance With Mixed Attention Mechanisms (AMs) and Deep Residual Shrinkage Networks (DRSNs)
Luying Zhang, Yuchen Bian, Peng Jiang, et al.
IEEE Sensors Journal (2024) Vol. 24, Iss. 4, pp. 5162-5171
Closed Access | Times Cited: 5
Application of machine learning to leakage detection of fluid pipelines in recent years: A review and prospect
Jianwu Chen, Xiao Wu, Zhibo Jiang, et al.
Measurement (2025), pp. 116857-116857
Closed Access
Characterization of Oil and Gas Pipeline Detection Signals with Adjustable Weak Magnetic Excitation
Luyao He, Licheng Han, M. Liu, et al.
Sensing and Imaging (2025) Vol. 26, Iss. 1
Closed Access
Application of Object Detection Algorithms in Non-Destructive Testing of Pressure Equipment: A Review
Wang Wei-hua, Jiugong Chen, Gangsheng Han, et al.
Sensors (2024) Vol. 24, Iss. 18, pp. 5944-5944
Open Access | Times Cited: 3
Adaptive fast Walsh-Hadamard transform for magnetic flux leakage signal of broken wire damage extraction under noise background
Shengping Huang, Zhong-Qiu Wang, Jianhua Yang, et al.
Nondestructive Testing And Evaluation (2024), pp. 1-21
Closed Access | Times Cited: 2
Identification method of internal leakage in nuclear power plants valves using convolutional block attention module
Xin Huang, Wenzhong Qu, Xiao Li
Nuclear Engineering and Design (2024) Vol. 424, pp. 113239-113239
Closed Access | Times Cited: 2
Development of a convolutional neural network model to predict the size and location of corrosion defects on pipelines based on magnetic flux leakage signals
Youqing Shen, Wenxing Zhou
International Journal of Pressure Vessels and Piping (2023) Vol. 207, pp. 105123-105123
Closed Access | Times Cited: 6
Automated matching and visualisation of magnetic flux leakage data in shale gas pipeline based on ICP and DBSCAN algorithm
Leyao Gu, Shanbi Peng, Enbin Liu, et al.
Nondestructive Testing And Evaluation (2024), pp. 1-25
Closed Access | Times Cited: 1
In-Line Inspection Methods and Tools for Oil and Gas Pipeline: A Review
Jia Zhang, Mingnan Sun, Lin Qin, et al.
International Journal of Pressure Vessels and Piping (2024), pp. 105409-105409
Closed Access | Times Cited: 1
Combining ultrasonic guided wave and low-frequency electromagnetic technology for defect detection in high-temperature Cr–Ni alloy furnace tubes
Chenyang Du, Xiaowei Li, Chang Liu, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 4
A Novel Defect Detection Method for Overhead Ground Wire
Yao Xiao, Lan Xiong, Zhanlong Zhang, et al.
Sensors (2023) Vol. 24, Iss. 1, pp. 192-192
Open Access | Times Cited: 3
Research on Internal Shape Anomaly Inspection Technology for Pipeline Girth Welds Based on Alternating Excitation Detection
Rui Li, Pengchao Chen, Jie Huang, et al.
Sensors (2023) Vol. 23, Iss. 17, pp. 7519-7519
Open Access | Times Cited: 2
A Study of the Influence of Steel Brushes in Rail Surface Magnetic Flux Leakage Detection Using Finite Elements Simulation
Wendong Gong, Muhammad Firdaus Akbar, Mimi Faisyalini Ramli, et al.
Lecture notes in electrical engineering (2024), pp. 247-254
Closed Access
Solving inverse problems in magnetic field leakage sensor array inspection of petroleum tank floor
Kamel Belkhiri, Tarik Bouchala, Abdelhak Abdou, et al.
STUDIES IN ENGINEERING AND EXACT SCIENCES (2024) Vol. 5, Iss. 1, pp. 2492-2508
Open Access
Probabilistic Fatigue Crack Growth Prediction for Pipelines with Initial Flaws
Youngjin Choi, Seung-Jung Lee
Buildings (2024) Vol. 14, Iss. 6, pp. 1775-1775
Open Access
Quantitative Evaluation of Deformation in High-Speed Magnetic Flux Leakage Signals for Weld Defects in Oil and Gas Pipelines
Lemei Ren, Wenlong Liu, Bo Zhu, et al.
Processes (2024) Vol. 12, Iss. 11, pp. 2396-2396
Open Access
The Optimization Method of Magnetic Flux Leakage Detection Equipment in Rail Surface Inspection Based on Finite Element Simulation
Wendong Gong, Muhammad Firdaus Akbar, Ghassan Nihad Jawad, et al.
Integrated ferroelectrics (2024) Vol. 240, Iss. 8-9, pp. 1205-1219
Closed Access
Deep learning
Mohammad Ali Ahmadi
Elsevier eBooks (2024), pp. 405-445
Closed Access
Detection and Dispersion Analysis of Water Globules in Oil Samples Using Artificial Intelligence Algorithms
Alexey N. Beskopylny, Антон Чепурненко, Besarion Meskhi, et al.
Biomimetics (2023) Vol. 8, Iss. 3, pp. 309-309
Open Access | Times Cited: 1
Magnetic flux leakage defect size estimation method based on physics-informed neural network
Xiong Yi, Shuai Liu, Litao Hou, et al.
Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences (2023) Vol. 382, Iss. 2264
Closed Access | Times Cited: 1
Inspection of Wire Ropes Based on Magnetic Flux Leakage Images by Using YOLOv5
Feiyang Pan, Yi‐Cheng Huang, Liyuan Ren, et al.
(2023), pp. 1-7
Closed Access
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