Evidence map›Paper›PMID 35408107›Full record

ArticleSensors (Basel, Switzerland)2022

Acoustic Emission Source Location Using Finite Element Generated Delta-T Mapping.

Han Yang, Bin Wang, Stephen Grigg, Ling Zhu, Dandan Liu, Ryan Marks

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
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

6 authors.

Han YangTWI Ltd., Granta Park, Great Abington, Cambridge CB21 6AL, UK.
Bin WangDepartment of Mechanical and Aerospace Engineering, Brunel University London, Uxbridge UB8 3PH, UK.ORCID 0000-0002-1398-6599
Stephen GriggTWI Ltd., Granta Park, Great Abington, Cambridge CB21 6AL, UK.
Ling ZhuKey Laboratory of High Performance Ship Technology (Wuhan University of Technology), Ministry of Education, Wuhan 430063, China.
Dandan LiuTWI Ltd., Granta Park, Great Abington, Cambridge CB21 6AL, UK.
Ryan MarksTWI Ltd., Granta Park, Great Abington, Cambridge CB21 6AL, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

One of the most significant benefits of Acoustic Emission (AE) testing over other Non-Destructive Evaluation (NDE) techniques lies in its damage location capability over a wide area. The delta-T mapping technique developed by researchers has been shown to enable AE source location to a high level of accuracy in complex structures. However, the time-consuming and laborious data training process of the delta-T mapping technique has prevented this technique from large-scale application on large complex structures. In order to solve this problem, a Finite Element (FE) method was applied to model training data for localization of experimental AE events on a complex plate. Firstly, the FE model was validated through demonstrating consistency between simulated data and the experimental data in the study of Hsu-Nielsen (H-N) sources on a simple plate. Then, the FE model with the same parameters was applied to a planar location problem on a complex plate. It has been demonstrated that FE generated delta-T mapping data can achieve a reasonable degree of source location accuracy with an average error of 3.88 mm whilst decreasing the time and effort required for manually collecting and processing the training data.

Indexed as

AcousticsFinite Element AnalysisAcoustic Emissioncomplex platedelta-T mappingfinite elementHsu-Nielsen sourcesNon-Destructive Evaluationsource locationStructural Health Monitoring

Identifiers

PMID35408107
PMCPMC9002672

What OpenQuestion holds

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Registered trials

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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.