Evidence map›Paper›PMID 34205033›Full record

ReviewSensors (Basel, Switzerland)2021

Pipeline In-Line Inspection Method, Instrumentation and Data Management.

Qiuping Ma, Guiyun Tian, Yanli Zeng, Rui Li, Huadong Song, Zhen Wang, Bin Gao, Kun Zeng

Open access · goldAbstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
13.7field-weighted citation impact, top 1% of its field
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

17 citing papers in PubMed, 207 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
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  9. Review
  10. Article
  11. Article
  12. A Review of Magnetic Flux Leakage Nondestructive Testing.Materials (Basel, Switzerland) · 2022
    Review
  13. Article
  14. Article
  15. Article
  16. Article
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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

8 authors at 3 institutions in 2 countries.

Qiuping MaSchool of Automation, University of Electronic Science and Technology of China, Chengdu 611731, China.
Guiyun TianSchool of Automation, University of Electronic Science and Technology of China, Chengdu 611731, China.ORCID 0000-0002-7563-1523
Yanli ZengShenyang Academy of Instrumentation Science, Shenyang 110043, China.
Rui LiPipeChina Northern Company, Langfang 065000, China.ORCID 0000-0001-5084-0276
Huadong SongShenyang Academy of Instrumentation Science, Shenyang 110043, China.
Zhen WangSchool of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.ORCID 0000-0002-9584-8586
Bin GaoSchool of Automation, University of Electronic Science and Technology of China, Chengdu 611731, China.ORCID 0000-0001-9993-1013
Kun ZengSchool of Automation, University of Electronic Science and Technology of China, Chengdu 611731, China.ORCID 0000-0002-5596-7599
University of Electronic Science and Technology of China · CNShenyang Conservatory of Music · CNNanjing University of Aeronautics and Astronautics · CN

Funding

National Natural Science Foundation of China 61960206010
6 · The paper itself

Abstract

Pipelines play an important role in the national/international transportation of natural gas, petroleum products, and other energy resources. Pipelines are set up in different environments and consequently suffer various damage challenges, such as environmental electrochemical reaction, welding defects, and external force damage, etc. Defects like metal loss, pitting, and cracks destroy the pipeline's integrity and cause serious safety issues. This should be prevented before it occurs to ensure the safe operation of the pipeline. In recent years, different non-destructive testing (NDT) methods have been developed for in-line pipeline inspection. These are magnetic flux leakage (MFL) testing, ultrasonic testing (UT), electromagnetic acoustic technology (EMAT), eddy current testing (EC). Single modality or different kinds of integrated NDT system named Pipeline Inspection Gauge (PIG) or un-piggable robotic inspection systems have been developed. Moreover, data management in conjunction with historic data for condition-based pipeline maintenance becomes important as well. In this study, various inspection methods in association with non-destructive testing are investigated. The state of the art of PIGs, un-piggable robots, as well as instrumental applications, are systematically compared. Furthermore, data models and management are utilized for defect quantification, classification, failure prediction and maintenance. Finally, the challenges, problems, and development trends of pipeline inspection as well as data management are derived and discussed.

Indexed as

Data ManagementTransportationAcousticsElectromagnetic Phenomenadata managementnon-destructive testing (NDT)pipeline inspectionpipeline integrity managementrobot

Identifiers

PMID34205033
PMCPMC8199892
OpenAlexW3171244254

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.