Evidence map›Paper›PMID 39459998›Full record

ArticleSensors (Basel, Switzerland)2024

Lamb Wave Probabilistic Damage Identification Based on the Exchanging-Element Time-Reversal Method.

Zeyu Shu, Jian He, Muping Hu, Zonghui Wu, Xiaodan Sun

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

5 authors.

Zeyu ShuCollege of Aerospace and Civil Engineering, Harbin Engineering University, Harbin 150001, China.ORCID 0000-0001-5789-6504
Jian HeCollege of Aerospace and Civil Engineering, Harbin Engineering University, Harbin 150001, China.ORCID 0000-0002-3715-8261
Muping HuCollege of Aerospace and Civil Engineering, Harbin Engineering University, Harbin 150001, China.
Zonghui WuCollege of Aerospace and Civil Engineering, Harbin Engineering University, Harbin 150001, China.
Xiaodan SunCollege of Aerospace and Civil Engineering, Harbin Engineering University, Harbin 150001, China.ORCID 0000-0002-7946-323X

Funding

the National Natural Science Foundation of China 52278297
6 · The paper itself

Abstract

The commonly used baseline-free Lamb wave damage identification methods often require a large amount of sensor data to eliminate the dependence on baseline signals. To improve the efficiency of damage localization, this paper proposes a new Lamb wave damage location method, namely the probabilistic exchanging-element time-reversal method (PEX-TRM), which is based on the exchanging-element time-reversal method (EX-TRM) and the probabilistic damage identification method. In this method, the influence of the damage wave packet migration on the correlation coefficient between the reconstructed signals of each sensing path and the initial excitation signal is analyzed, and the structure is divided into multiple regional units corresponding to the damage to locate damage. In addition, the influence of the number of sensing paths on the location accuracy is also analyzed. A method of damage probability imaging based on structural symmetry is proposed to enhance location accuracy in the case of sparse sensing paths. The experimental and simulation results verify that the method can achieve damage location with fewer excitation times. Moreover, this method can avoid the problem that the damage wave packet is difficult to extract, improve the efficiency of damage location, and promote the engineering application of the Lamb wave damage location method.

Indexed as

correlation analysisdamage locationexchanging-element time-reversal methodLamb wavesprobabilistic damage imaging

Identifiers

PMID39459998
PMCPMC11510728

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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.