Evidence map›Paper›PMID 39796881›Full record

ArticleSensors (Basel, Switzerland)2024

Enhanced Sensor Placement Optimization and Defect Detection in Structural Health Monitoring Using Hybrid PI-DEIM Approach.

Minyoung Yun, Mikhael Tannous, Chady Ghnatios, Eivind Fonn, Trond Kvamsdal, Francisco Chinesta

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Minyoung YunPIMM Research Laboratory, UMR 8006 CNRS-ENSAM-CNAM, Arts et Metiers Institute of Technology, 151 Boulevard de l'Hôpital, 75013 Paris, France.ORCID 0000-0003-3482-0149
Mikhael TannousPIMM Research Laboratory, UMR 8006 CNRS-ENSAM-CNAM, Arts et Metiers Institute of Technology, 151 Boulevard de l'Hôpital, 75013 Paris, France.ORCID 0000-0003-3155-9213
Chady GhnatiosMechanical Engineering Department, University of North Florida, 1 UNF Drive, Jacksonville, FL 32224, USA.ORCID 0000-0001-7100-3813
Eivind FonnDepartment of Mathematics and Cybernetics, SINTEF Digital, Kloebuveien 153, 7465 Trondheim, Norway.
Trond KvamsdalDepartment of Mathematics and Cybernetics, SINTEF Digital, Kloebuveien 153, 7465 Trondheim, Norway.
Francisco ChinestaPIMM Research Laboratory, UMR 8006 CNRS-ENSAM-CNAM, Arts et Metiers Institute of Technology, 151 Boulevard de l'Hôpital, 75013 Paris, France.

Funding

This project has received funding from the European Defense Fund (EDF) under grant 320 agreement 101103257 dTHOR-EDF-2021-NAVALR-2 (https://dthor.eu/). Grant 320 agreement 101103257 dTHOR-EDF-2021-NAVALR-2 (https://dthor.eu/).
6 · The paper itself

Abstract

This work introduces a novel methodology for identifying critical sensor locations and detecting defects in structural components. Initially, a hybrid method is proposed to determine optimal sensor placements by integrating results from both the discrete empirical interpolation method (DEIM) and the random permutation features importance technique (PI). Subsequently, the identified sensors are utilized in a novel defect detection approach, leveraging a semi-intrusive reduced order modeling and genetic search algorithm for fast and reliable defect detection. The proposed algorithm has successfully located defects with low error, especially when using hybrid sensors, which combine the most critical sensors identified through both PI and DEIM. This hybrid method identifies defects with the lowest errors compared to using either the PI or DEIM methods alone.

Indexed as

discrete empirical interpolation methodmachine learningoptimal sensor placementrandom permutation features importance method

Identifiers

PMID39796881
PMCPMC11723021

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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.