Non-Destructive Testing Technology for Natural Gas Pipeline Wall Thickness Measurement: A Comprehensive Review

With the gradual increase in demand for natural gas, the scale of global natural gas pipeline (NGP) construction is also gradually increasing. However, with the gradual increase in NGP laying, the reduction in pipeline wall thickness is the main cause of major safety accidents. In order to provide researchers with a more comprehensive understanding of the current mainstream NGP wall thickness detection technology, this study elucidates this technology. A comparative analysis is conducted on mainstream NGP wall thickness detection technologies from multiple perspectives, including their technical principles, advantages, disadvantages, and engineering application limitations. In addition, with the rapid development of artificial intelligence (AI) technology, pipeline wall thickness detection has also become one of its application scenarios. In this study, the application of AI technology in the field of pipeline wall thickness detection is evaluated in detail, including in relation to intelligent signal denoising and feature extraction, AI-driven quantitative inversion of pipeline wall thickness, intelligent identification, classification and quantification of corrosion defects. An analysis is conducted on the challenges of applying AI in the field of pipeline inspection. The purpose of this study is to understand the wall thickness detection technology and future development trends of NGPs.

Authors

Institutions

Publication Details

Journal
Processes
Published
2026-09-22
DOI
https://doi.org/10.3390/pr14193028
Primary Topic
Structural Integrity and Reliability Analysis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Non-Destructive Testing Technology for Natural Gas Pipeline Wall Thickness Measurement: A Comprehensive Review

Weibiao Qiao, Yibo Wang, Qingshan Feng, Luyao Shi et al.
Processes
Structural Integrity and Reliability Analysis
article

Non-Destructive Testing Technology for Natural Gas Pipeline Wall Thickness Measurement: A Comprehensive Review

Weibiao Qiao, Yibo Wang, Qingshan Feng, Luyao Shi, Yuqin Wang, Shaohua Dong, shaozhu liu
article en

Abstract

With the gradual increase in demand for natural gas, the scale of global natural gas pipeline (NGP) construction is also gradually increasing. However, with the gradual increase in NGP laying, the reduction in pipeline wall thickness is the main cause of major safety accidents. In order to provide researchers with a more comprehensive understanding of the current mainstream NGP wall thickness detection technology, this study elucidates this technology. A comparative analysis is conducted on mainstream NGP wall thickness detection technologies from multiple perspectives, including their technical principles, advantages, disadvantages, and engineering application limitations. In addition, with the rapid development of artificial intelligence (AI) technology, pipeline wall thickness detection has also become one of its application scenarios. In this study, the application of AI technology in the field of pipeline wall thickness detection is evaluated in detail, including in relation to intelligent signal denoising and feature extraction, AI-driven quantitative inversion of pipeline wall thickness, intelligent identification, classification and quantification of corrosion defects. An analysis is conducted on the challenges of applying AI in the field of pipeline inspection. The purpose of this study is to understand the wall thickness detection technology and future development trends of NGPs.

ProcessesVol. 14(19)
China University of Petroleum, Beijing (CN), Yanshan University (CN)
Openalex Percentile: Top 20%
Structural Integrity and Reliability Analysis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.