Evidence map›Paper›PMID 36081010›Full record

ArticleSensors (Basel, Switzerland)2022

Research and Application of Multi-Mode Joint Monitoring System for Shaft Wall Deformation.

Xinqiu Fang, Fan Zhang, Zongshen Shi, Minfu Liang, Yang Song

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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. 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.

Xinqiu FangSchool of Mines, China University of Mining and Technology, Xuzhou 221116, China.
Fan ZhangSchool of Mines, China University of Mining and Technology, Xuzhou 221116, China.
Zongshen ShiXinji Energy Co., Ltd., China National Coal Group Corp., Bozhou 236000, China.
Minfu LiangSchool of Mines, China University of Mining and Technology, Xuzhou 221116, China.
Yang SongSchool of Mines, China University of Mining and Technology, Xuzhou 221116, China.

Funding

the National Natural Science Foundation of China 51874276the National Natural Science Foundation of China 52004273the Natural Science Foundation of Jiangsu Province BK20200639
6 · The paper itself

Abstract

The mine shaft is an important channel linking the underground with the surface, undertaking important functions such as personnel and material transportation and ventilation. Thus the shaft, known as the throat of the mine, is the production hub of the whole mine. Since 1980, damage to coal mine shafts has occurred in many areas of China, which has seriously impacted the safety of mine production. Therefore, real-time monitoring of the shaft wall condition is necessary. However, the traditional monitoring method cannot achieve long-term, continuous and stable monitoring of the shaft wall due to the harsh production environment downhole. Hence, a multi-mode joint sensing system for shaft wall deformation and damage is proposed, which is mainly based on FBG sensing and supplemented by vibrating-string sensing. The principle of FBG sensing is that when the external environment such as temperature, pressure and strain changes, the characteristics of light transmission in the FBG such as wavelength, phase and amplitude will also change accordingly. Using the linear relationship between the strain and the wavelength shift of the FBG, the strain of the measured structure is obtained by calculation. Firstly, this paper introduces the basic situations of the mine and analyzes the causes shaft damage. Then the vertical and circumferential theoretical values at different shaft depths are derived in combination with the corresponding force characteristics. Moreover, a four-layer strain transfer structure model of the shaft consisting of the fiber, the protective layer, the bonding layer and the borehole wall is established, which leads to the derivation of the strain transfer relational expression for the surface-mounted FBG sensing on the shaft wall. The strain-sensing transfer law and the factors influencing the strain-sensing transfer of the surface-mounted FBG on the shaft wall are analyzed. The order of key factors influencing the strain-sensing transfer is obtained by numerical simulation: the radius of the protective layer, the length of the FBG paste, and the elastic modulus of the adhesive layer. The packaging parameters with the best strain-sensing transfer of the surface-mounted FBG on the shaft wall are determined. A total of six horizontal level monitoring stations are arranged in a coal mine auxiliary shaft. Through the comprehensive analysis of the sensing data of the two sensors, the results show that the average shaft wall strain-transfer efficiency measured by the FBG sensor reaches 94.02%. The relative average error with the theoretical derivation of shaft wall transfer efficiency (98.6%) is 4.65%, which verifies the strain transfer effect of the surface-mounted FBG applied to the shaft wall. The shaft wall's deformation monitoring system with FBG sensing as the main and vibrating-string sensing as the supplement is important to realize the early warning of well-wall deformation and further research of the shaft wall rupture mechanism.

Indexed as

monitoring systemshaft wall rupturestrain analysisthe FBG

Identifiers

PMID36081010
PMCPMC9460250

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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.