Evidence map›Paper›PMID 38732805›Full record

ReviewSensors (Basel, Switzerland)2024

Systematic Evaluation of Ultrasonic In-Line Inspection Techniques for Oil and Gas Pipeline Defects Based on Bibliometric Analysis.

Jie Huang, Pengchao Chen, Rui Li, Kuan Fu, Yanan Wang, Jinyao Duan, Zhenlin Li

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Jie HuangCollege of Mechanical and Storage and Transportation Engineering, China University of Petroleum (Beijing), Beijing 102249, China.ORCID 0009-0004-6020-1004
Pengchao ChenGeneral Research Institute, China Oil & Gas Pipeline Network Corporation, Langfang 065000, China.
Rui LiGeneral Research Institute, China Oil & Gas Pipeline Network Corporation, Langfang 065000, China.ORCID 0000-0001-5084-0276
Kuan FuGeneral Research Institute, China Oil & Gas Pipeline Network Corporation, Langfang 065000, China.
Yanan WangGeneral Research Institute, China Oil & Gas Pipeline Network Corporation, Langfang 065000, China.
Jinyao DuanGeneral Research Institute, China Oil & Gas Pipeline Network Corporation, Langfang 065000, China.
Zhenlin LiCollege of Mechanical and Storage and Transportation Engineering, China University of Petroleum (Beijing), Beijing 102249, China.

Funding

National Key R&D Program of China 2022YFB3207600, 2022YFC3070100Science and Technology Research Project of PipeChina WZXGL202104, AQWH202203
6 · The paper itself

Abstract

The global reliance on oil and gas pipelines for energy transportation is increasing. As the pioneering review in the field of ultrasonic defect detection for oil and gas pipelines based on bibliometric methods, this study employs visual analysis to identify the most influential countries, academic institutions, and journals in this domain. Through cluster analysis, it determines the primary trends, research hotspots, and future directions in this critical field. Starting from the current global industrial ultrasonic in-line inspection (ILI) detection level, this paper provides a flowchart for selecting detection methods and a table for defect comparison, detailing the comparative performance limits of different detection devices. It offers a comprehensive perspective on the latest ultrasonic pipeline detection technology from laboratory experiments to industrial practice.

Indexed as

bibliometricsdefectsin-line inspectionoil and gaspipelineultrasonic testing

Identifiers

PMID38732805
PMCPMC11085684

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.