Evidence map›Paper›PMID 37765878›Full record

ArticleSensors (Basel, Switzerland)2023

π-FBG Fiber Optic Acoustic Emission Sensor for the Crack Detection of Wind Turbine Blades.

Qi Yan, Xingchen Che, Shen Li, Gensheng Wang, Xiaoying Liu

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. 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. Review
  4. 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

5 authors.

Qi YanSchool of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China.
Xingchen CheSchool of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China.
Shen LiSchool of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China.
Gensheng WangSPIC Jiangxi Electric Power Co., Ltd., Nanchang 330096, China.
Xiaoying LiuSchool of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China.ORCID 0000-0002-8898-8898

Funding

Shenzhen Science and Technology Programe GJHZ20210705142538004
6 · The paper itself

Abstract

Wind power is growing rapidly as a green and clean energy source. As the core part of a wind turbine, the blades are subjected to enormous stress in harsh environments over a long period of time and are therefore extremely susceptible to damage, while at the same time, they are costly, so it is important to monitor their damage in a timely manner. This paper is based on the detection of blade damage using acoustic emission signals, which can detect early minor damage and internal damage to the blades. Instead of conventional piezoelectric sensors, we use fiber optic gratings as sensing units, which have the advantage of small size and corrosion resistance. Furthermore, the sensitivity of the system is doubled by replacing the conventional FBG (fiber Bragg grating) with a π-phase-shifted FBG. For the noise problem existing in the system, this paper combines the traditional WPD (wavelet packet decomposition) denoising method with EMD (empirical mode decomposition) to achieve a better noise reduction effect. Finally, small wind turbine blades are used in the experiment and their acoustic emission signals with different damage are collected for feature analysis, which sets the stage for the subsequent detection of different damage degrees and types.

Indexed as

acoustic emissiondamage detectionwind turbine bladesWPD combined with EMDπ-FBG

Identifiers

PMID37765878
PMCPMC10535792

What OpenQuestion holds

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LicenceCC BY
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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.