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Recent Advances in Optical Sensor for Mining

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Optical Sensors".

Deadline for manuscript submissions: 25 February 2025 | Viewed by 14208

Special Issue Editors

Dr. Minfu Liang

E-Mail Website
Guest Editor
1. School of Mines, China University of Mining and Technology, Xuzhou 221116, China
2. Research Center of Intelligent Mining, China University of Mining and Technology, Xuzhou 221116, China
Interests: intelligent mining; fiber optic sensing monitoring of coal mining information; development and application of fiber optic sensors for coal mines
Prof. Dr. Xinqiu Fang

E-Mail Website
Guest Editor
1. School of Mines, China University of Mining and Technology, Xuzhou 221116, China
2. Research Center of Intelligent Mining, China University of Mining and Technology, Xuzhou 221116, China
Interests: Intelligent mining; fiber optic sensing technology for coal mines; application of fiber Bragg grating sensors/fiber optic sensors in coal mines
Dr. Gang Cheng

E-Mail Website
Guest Editor
School of Computer Science, North China Institute of Science and Technology, Beijing 101601, China
Interests: geotechnical engineering monitoring based on distributed fiber optic sensing technology; remote data acquisition; geological disaster evaluation based on the Internet of Things
Special Issues, Collections and Topics in MDPI journals
Dr. Qiang Yuan

E-Mail Website
Guest Editor
State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China
Interests: optical fiber monitoring for mining process dynamics

Special Issue Information

Dear Colleagues,

The intelligent recognition of major coal mine disasters, early warning signs, and hidden risk is the foundation of coal mine safety. Clarifying the distribution characteristics, influencing laws, and evolution mechanisms of coal mine disasters, and achieving comprehensive perception, dynamic monitoring, intelligent early warning systems, and graded response with regard to coal mine information and other innovative technologies will become the key principles of the intelligent construction of coal mines.

With the in-depth application of optical fiber sensing technology in the field of engineering monitoring, its passive, wide-area, refined, and anti-interference characteristics make it one of the top choices for engineering protection in disaster environments. As an intrinsically safe sensing and monitoring method, optical fiber sensing technology has natural advantages in coal mine applications and has made significant progress in areas such as mine pressure monitoring, roadway support, strata control, goaf fires, coal mine micro-seismic activity monitoring, and underground equipment safety. Its application in mining engineering can help further realize the progression from the perception to the cognition of mine disasters and accidents, which will effectively promote efficient, high-yield, green, and safe coal mining and can become a key solution to promote the intelligent construction of coal mines.

This Special Issue entitled "Recent Advances in Optical Sensors for Mining" aims to provide selected contributions on advances in the theory, experimentation, and application of fiber optic sensing technology in mining engineering monitoring. Meanwhile, it seeks to deepen the application of optical fiber sensing technology in the intelligent construction of coal mines. Potential topics include, but are not limited to, the following:

  • Advances in sensors and sensing technologies (fiber Bragg grating sensing, distributed fiber optic sensing, multi-parameter optical fiber sensors for mining, etc.);
  • Opto-electronic monitoring in mining engineering (deformation monitoring, stress monitoring, temperature monitoring, vibration monitoring, seepage monitoring, etc.);
  • Artificial intelligence algorithms for fiber optic monitoring data processing in mine engineering;
  • Big data mining and risk assessment of mine engineering monitoring;
  • Fine monitoring technology of underground mining;
  • Intelligent monitoring and maintenance of mine engineering;
  • Monitoring practice and typical case analysis of major mine engineering;
  • Intelligent real-time monitoring technology for mine engineering.

Dr. Minfu Liang
Prof. Dr. Xinqiu Fang
Dr. Gang Cheng
Dr. Qiang Yuan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • fiber optic sensing
  • optical sensor
  • mining disaster monitoring
  • opto-electronic monitoring
  • artificial intelligence algorithm
  • big data mining
  • risk assessment
  • coal mine disaster early warning systems.

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34 pages, 14710 KiB  
Article
Research on Spatiotemporal Continuous Information Perception of Overburden Compression–Tensile Strain Transition Zone during Mining and Integrated Safety Guarantee System
by Gang Cheng, Ziyi Wang, Bin Shi, Tianlu Cai, Minfu Liang, Jinghong Wu and Qinliang You
Sensors 2024, 24(17), 5856; https://doi.org/10.3390/s24175856 - 9 Sep 2024
Viewed by 1008
Abstract
The mining of deep underground coal seams induces the movement, failure, and collapse of the overlying rock–soil body, and the development of this damaging effect on the surface causes ground fissures and ground subsidence on the surface. To ensure safety throughout the life [...] Read more.
The mining of deep underground coal seams induces the movement, failure, and collapse of the overlying rock–soil body, and the development of this damaging effect on the surface causes ground fissures and ground subsidence on the surface. To ensure safety throughout the life cycle of the mine, fully distributed, real-time, and continuous sensing and early warning is essential. However, due to mining being a dynamic process with time and space, the overburden movement and collapse induced by mining activities often have a time lag effect. Therefore, how to find a new way to resolve the issue of the existing discontinuous monitoring technology of overburden deformation, obtain the spatiotemporal continuous information of the overlying strata above the coal seam in real time and accurately, and clarify the whole process of deformation in the compression–tensile strain transition zone of overburden has become a key breakthrough in the investigation of overburden deformation mechanism and mining subsidence. On this basis, firstly, the advantages and disadvantages of in situ observation technology of mine rock–soil body were compared and analyzed from the five levels of survey, remote sensing, testing, exploration, and monitoring, and a deformation and failure perception technology based on spatiotemporal continuity was proposed. Secondly, the evolution characteristics and deformation failure mechanism of the compression–tensile strain transition zone of overburden were summarized from three aspects: the typical mode of deformation and collapse of overlying rock–soil body, the key controlling factors of deformation and failure in the overburden compression–tensile strain transition zone, and the stability evaluation of overburden based on reliability theory. Finally, the spatiotemporal continuous perception technology of overburden deformation based on DFOS is introduced in detail, and an integrated coal seam mining overburden safety guarantee system is proposed. The results of the research can provide an important evaluation basis for the design of mining intensity, emergency decisions, and disposal of risks, and they can also give important guidance for the assessment of ground geological and ecological restoration and management caused by underground coal mining. Full article
(This article belongs to the Special Issue Recent Advances in Optical Sensor for Mining)
►▼ Show Figures

Figure 1

Figure 1
<p>Structure of energy consumption in China, 2019–2023.</p> Full article ">Figure 2
<p>Mine accidents and geological disasters caused by mining: (<b>a</b>) Roadway deformation, (<b>b</b>) Mine water inrush, (<b>c</b>) Ground subsidence, and (<b>d</b>) Induced landslide.</p> Full article ">Figure 3
<p>Statistics of coal mine accidents in China, 2014–2023.</p> Full article ">Figure 4
<p>Development and evolution of stope structure model.</p> Full article ">Figure 5
<p>Theoretical model of overburden stress distribution. (Where: σ<sub>z</sub> is the peak stress of coal pillars; <span class="html-italic">k</span> is the stress concentration coefficient; γ is the average bulk density of the overlying rock layer of the coal seam, k/Nm<sup>3</sup>; h is buried in the coal seam deep, m.)</p> Full article ">Figure 6
<p>Typical types of overburden deformation: (<b>a</b>) Bending and tensile failure, (<b>b</b>) Overall shear failure, and (<b>c</b>) Shear and sliding failure.</p> Full article ">Figure 7
<p>The process of gray relational analysis.</p> Full article ">Figure 8
<p>Schematic diagram of probability integration method.</p> Full article ">Figure 9
<p>Prediction process of overburden stability.</p> Full article ">Figure 10
<p>Bayesian-based overburden rock stability evaluation.</p> Full article ">Figure 11
<p>Principle of FBG and DFOS technologies: (<b>a</b>) FBG (Fiber Brag Grating), (<b>b</b>) UWFBG ((Ultra-Weak Fiber Bragg Grating), (<b>c</b>) BOTDR (Brillouin Optical Time Domain Reflectometry), and (<b>d</b>) DTS (Distributed Temperature Sensing).</p> Full article ">Figure 12
<p>Temperature test results at different leakage pressures (<b>a</b>–<b>d</b>). Strain changes in sensing cables in different layers (leakage pressure of 1 MPa). ① represents the bottom layer; ② represents the middle layer; ③ represents the top layer.</p> Full article ">Figure 13
<p>Set-up of digital BOFDA system (“DAC” represents Digital to Analog Converter; “ADC” represents Analog to Digital Converter).</p> Full article ">Figure 14
<p>Monitoring system layout and result.</p> Full article ">Figure 15
<p>Strain curve of decoupling test.</p> Full article ">Figure 16
<p>Strain distribution of overburden deformation.</p> Full article ">Figure 17
<p>Backfilling material test model.</p> Full article ">Figure 18
<p>Sensing cable layout for the ground monitoring system: (<b>a</b>) Cable layout, (<b>b</b>) Borehole backfill, (<b>c</b>) Cable coupled with borehole, and (<b>d</b>) Cable protection.</p> Full article ">Figure 19
<p>Sensing cable layout for the underground monitoring system: (<b>a</b>) Cable layout, (<b>b</b>) Borehole drill, (<b>c</b>) Grouting, (<b>d</b>) Cable implant, (<b>e</b>) Optic fiber monitoring result, and (<b>f</b>) Electrical method monitoring result.</p> Full article ">Figure 20
<p>The layout process of pullout test and distribution of sensing cable strain data.</p> Full article ">Figure 21
<p>The three-stage model of pullout force–displacement relationship. The blue lines indicate different pull-out force distributions, and the five Roman numerals represent the five stages of pure elasticity, elasticity-softening, pure softening, softening-residual, and pure residual.</p> Full article ">Figure 22
<p>Coupling test for sensing cable–soil under controllable confining pressure: (<b>a</b>) diagram of test device; (<b>b</b>) curves of ground subsidence and calculated values of ground pressure [<a href="#B40-sensors-24-05856" class="html-bibr">40</a>].</p> Full article ">Figure 23
<p>Integrated safety guarantee system for coal mining.</p> Full article ">Figure 24
<p>Neural perception of the rock–soil body.</p> Full article ">Figure 25
<p>Self-diagnostic self-healing FBG sensing network system. The optical fiber has the ability of self-healing and self-diagnosis, and the cross sign indicates that after the upper fiber is broken, it can be switched to the following fiber for monitoring, so as to achieve uninterrupted monitoring. The dash lines indicate that the two fibers can be switched.</p> Full article ">Figure 26
<p>Modeling of overburden deformation prediction based on machine learning.</p> Full article ">Figure 27
<p>Early warning levels for overburden stability.</p> Full article ">Figure 28
<p>Integrated spatiotemporal continuous sensing system.</p> Full article ">
15 pages, 4475 KiB  
Article
Evolution Characteristics of Void in the Caving Zone Using Fiber Optic Sensing
by Jing Chai, Fengqi Qiu, Lei Zhu and Dingding Zhang
Sensors 2024, 24(2), 478; https://doi.org/10.3390/s24020478 - 12 Jan 2024
Cited by 1 | Viewed by 859
Abstract
Addressing the issue of low filling efficiency in gangue slurry backfilling due to unclear evolution characteristics of voids in the overlying collapsed rock mass during mining, this study utilizes fiber optic sensing technology to monitor real-time strain changes within the rock mass. It [...] Read more.
Addressing the issue of low filling efficiency in gangue slurry backfilling due to unclear evolution characteristics of voids in the overlying collapsed rock mass during mining, this study utilizes fiber optic sensing technology to monitor real-time strain changes within the rock mass. It proposes a void zoning method based on fiber optic sensing for mining the overlying rock and, in combination with physical model experiments, systematically investigates the dimensions, distribution, and deformation characteristics of rock mass voids. By analyzing fiber optic sensing data, the correlation between the rate of void expansion and the stress state of the rock mass is revealed. The research results demonstrate that as mining progresses, the internal voids of the rock mass gradually expand, exhibiting complex spatial distribution patterns. During the mining process, the expansion of voids within the overlying collapsed rock mass is closely related to the stress state of the rock mass. The rate of void expansion is influenced by changes in stress, making stress regulation a key factor in preventing void expansion and rock mass instability. The application of fiber optic sensing technology allows for more accurate monitoring of changes in rock mass voids, enabling precise zoning of voids in the overlying collapsed rock mass during mining. This zoning method has been validated against traditional theoretical calculations and experimental results. This research expands our understanding of the evolution characteristics of voids in overlying collapsed rock mass and provides valuable reference for backfilling engineering practices and backfilling parameter optimization. Full article
(This article belongs to the Special Issue Recent Advances in Optical Sensor for Mining)
►▼ Show Figures

Figure 1

Figure 1
<p>Schematic Diagram of Slurry Filling Mining.</p> Full article ">Figure 2
<p>Partition Characteristics of Void Development in the Overlying Rock Collapse Zone.</p> Full article ">Figure 3
<p>Characteristics of Forces on Vertical Fiber Optics.</p> Full article ">Figure 4
<p>Total station survey point layout.</p> Full article ">Figure 5
<p>Similar model fiber optic test system.</p> Full article ">Figure 6
<p>Distributed fiber optic strain uncertainty test.</p> Full article ">Figure 7
<p>Pressure testing system. (<b>a</b>) Pressure Sensor. (<b>b</b>) Pressure testing device.</p> Full article ">Figure 8
<p>Model Excavation Process. (<b>a</b>) The 37th Excavation. (<b>b</b>) The 44th Excavation.</p> Full article ">Figure 9
<p>Roof Layer Subsidence Curves. (<b>a</b>) 99 mm above the coal seam. (<b>b</b>) 146 mm above the coal seam.</p> Full article ">Figure 10
<p>Porosity Distribution Curves in the Collapse Zone.</p> Full article ">Figure 11
<p>Vertical Fiber V1 Test Results. (<b>a</b>) Working face near the fiber optic cable. (<b>b</b>) Working face over the fiber optic cable. (<b>c</b>) Working face away from the fiber optic cable.</p> Full article ">Figure 12
<p>Stress Distribution Curve for the Floor Pressure Sensor.</p> Full article ">Figure 13
<p>Fiber Optic Strain Distribution Curves.</p> Full article ">
13 pages, 4105 KiB  
Article
Research on Coal and Gangue Recognition Based on the Improved YOLOv7-Tiny Target Detection Algorithm
by Yiping Sui, Lei Zhang, Zhipeng Sun, Weixun Yi and Meng Wang
Sensors 2024, 24(2), 456; https://doi.org/10.3390/s24020456 - 11 Jan 2024
Cited by 1 | Viewed by 1304
Abstract
The recognition technology of coal and gangue is one of the key technologies of intelligent mine construction. Aiming at the problems of the low accuracy of coal and gangue recognition models and the difficult recognition of small-target coal and gangue caused by low-illumination [...] Read more.
The recognition technology of coal and gangue is one of the key technologies of intelligent mine construction. Aiming at the problems of the low accuracy of coal and gangue recognition models and the difficult recognition of small-target coal and gangue caused by low-illumination and high-dust environments in the coal mine working face, a coal and gangue recognition model based on the improved YOLOv7-tiny target detection algorithm is proposed. This paper proposes three model improvement methods. The coordinate attention mechanism is introduced to improve the feature expression ability of the model. The contextual transformer module is added after the spatial pyramid pooling structure to improve the feature extraction ability of the model. Based on the idea of the weighted bidirectional feature pyramid, the four branch modules in the high-efficiency layer aggregation network are weighted and cascaded to improve the recognition ability of the model for useful features. The experimental results show that the average precision mean of the improved YOLOv7-tiny model is 97.54%, and the FPS is 24.73 f·s−1. Compared with the Faster-RCNN, YOLOv3, YOLOv4, YOLOv4-VGG, YOLOv5s, YOLOv7, and YOLOv7-tiny models, the improved YOLOv7-tiny model has the highest recognition rate and the fastest recognition speed. Finally, the improved YOLOv7-tiny model is verified by field tests in coal mines, which provides an effective technical means for the accurate identification of coal and gangue. Full article
(This article belongs to the Special Issue Recent Advances in Optical Sensor for Mining)
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Figure 1

Figure 1
<p>Improved YOLOv7-tiny network structure.</p> Full article ">Figure 2
<p>Coordinate attention mechanism module structure diagram.</p> Full article ">Figure 3
<p>Contextual transformer module structure diagram.</p> Full article ">Figure 4
<p>ELAN and Bi-ELAN structures. (<b>a</b>) ELAN; (<b>b</b>) Bi-ELAN.</p> Full article ">Figure 5
<p>Loss value change curve during training.</p> Full article ">Figure 6
<p>Improved YOLOv7-tiny and YOLOv7-tiny model coal and gangue identification evaluation index change curves. (<b>a</b>) Precision curve; (<b>b</b>) Recall rate curve; (<b>c</b>) F1 value curve; (<b>d</b>) Precision and recall rate curves.</p> Full article ">Figure 7
<p>The mAP curves for different model comparison experiments.</p> Full article ">Figure 8
<p>Coal and gangue identification results from the different models.</p> Full article ">Figure 9
<p>Image recognition results for coal and gangue on belt conveyor at the working face. (<b>a</b>) Identification results of coal and gangue; (<b>b</b>) Location rendering of the coal and gangue.</p> Full article ">
16 pages, 10403 KiB  
Article
Research on Similarity Simulation Experiment of Mine Pressure Appearance in Surface Gully Working Face Based on BOTDA
by Dingding Zhang, Zhiming Huang, Zhe Ma, Jianfeng Yang and Jing Chai
Sensors 2023, 23(22), 9063; https://doi.org/10.3390/s23229063 - 9 Nov 2023
Cited by 6 | Viewed by 1016
Abstract
In order to study the mountain deflection characteristics and the pressure law of the working face after the mining of a shallow coal seam under the valley terrain, a geometric size of 5.0 × 0.2 × 1.33 m is used in the physical [...] Read more.
In order to study the mountain deflection characteristics and the pressure law of the working face after the mining of a shallow coal seam under the valley terrain, a geometric size of 5.0 × 0.2 × 1.33 m is used in the physical similarity model. Brillouin optical time domain analysis (BOTDA) technology is applied to a similar physical model experiment to monitor the internal strain of the overlying rock. In this paper, the strain law of the horizontal optical fiber at different stages of the instability of the mountain structure is analyzed. Combined with the measurement of the strain field on the surface of the model via digital image correlation (DIC) technology, the optical fiber strain characteristics of the precursor of mountain instability are given. The optical fiber characterization method of working face pressure is proposed, and the working face pressures at different mining stages in gully terrain are characterized. Finally, the relationship between the deflection instability of the mountain and the strong ground pressure on the working face is discussed. The sudden increase in the strain peak point of the horizontally distributed optical fiber strain curve can be used to distinguish the strong ground pressure. At the same time, this conclusion is verified by comparing the measured underground ground pressure values. The research results can promote the application of optical fiber sensing technology in the field of mine engineering. Full article
(This article belongs to the Special Issue Recent Advances in Optical Sensor for Mining)
►▼ Show Figures

Figure 1

Figure 1
<p>Characteristics of surface gullies and the roadway layout in 010204 working face.</p> Full article ">Figure 2
<p>Similar physical model.</p> Full article ">Figure 3
<p>Experiment monitoring system.</p> Full article ">Figure 4
<p>BOTDA sensing technology.</p> Full article ">Figure 5
<p>Caving characteristics of overlying strata in back ditch mining stage.</p> Full article ">Figure 6
<p>Caving characteristics of overlying strata in mountaintop mining.</p> Full article ">Figure 7
<p>Caving characteristics of overlying strata in the trench mining stage.</p> Full article ">Figure 8
<p>Strain curve of the horizontal optical fiber H1.</p> Full article ">Figure 9
<p>Strain curve of vertical optical fiber V1: (<b>a</b>) working face close to V1; (<b>b</b>) working face passing through V1; (<b>c</b>) working face away from V1.</p> Full article ">Figure 10
<p>Strain curve of vertical optical fiber V2: (<b>a</b>) working face close to V2; (<b>b</b>) working face passing through V2; (<b>c</b>) working face away from V2.</p> Full article ">Figure 11
<p>Working face advancing from 52.0 cm to 82.0 cm: (<b>a</b>) diagram of subcritical layer breakage; (<b>b</b>) fiber optic strain curve variation diagram.</p> Full article ">Figure 12
<p>Working face advancing from 82.0 cm to 124.0 cm: (<b>a</b>) diagram of sub critical layer breakage; (<b>b</b>) fiber optic strain curve variation diagram.</p> Full article ">Figure 13
<p>Mountaintop mining stage: (<b>a</b>) breakage of sub critical layers before mountain deflection; (<b>b</b>) change in strain curve before mountain deflection; (<b>c</b>) surface strain changes before mountain deflection; (<b>d</b>) breaking of subcritical layers after mountain deflection; (<b>e</b>) changes in the fiber optic strain curve after mountain deflection; (<b>f</b>) surface strain changes after mountain rotation.</p> Full article ">Figure 13 Cont.
<p>Mountaintop mining stage: (<b>a</b>) breakage of sub critical layers before mountain deflection; (<b>b</b>) change in strain curve before mountain deflection; (<b>c</b>) surface strain changes before mountain deflection; (<b>d</b>) breaking of subcritical layers after mountain deflection; (<b>e</b>) changes in the fiber optic strain curve after mountain deflection; (<b>f</b>) surface strain changes after mountain rotation.</p> Full article ">Figure 14
<p>Stage of trench mining: (<b>a</b>) advance to 238.0 cm overlying rock fracture situation; (<b>b</b>) fiber optic strain change; (<b>c</b>) surface strain variation in overlying rock.</p> Full article ">Figure 15
<p>Comparison between fiber frequency shift variation and support resistance.</p> Full article ">Figure 16
<p>Periodic weighting step of working face.</p> Full article ">Figure 17
<p>Variation curve of peak point of strain of horizontal optical fiber H1.</p> Full article ">Figure 18
<p>Comparative analysis diagram of peak point change in horizontal optical fiber and pressure on working face.</p> Full article ">
14 pages, 3795 KiB  
Article
Research on an Intelligent Mining Complete System of a Fully Mechanized Mining Face in Thin Coal Seam
by Bo Ren, Ke Ding, Lianguo Wang, Shuai Wang, Chongyang Jiang and Jiaxing Guo
Sensors 2023, 23(22), 9034; https://doi.org/10.3390/s23229034 - 8 Nov 2023
Cited by 5 | Viewed by 2400
Abstract
The mining environment of thin coal seam working faces is generally harsh, the labor intensity is high, and the production efficiency is low. Previous studies have shown that thin coal seam mining finds it difficult to follow machines, does not have complete sets [...] Read more.
The mining environment of thin coal seam working faces is generally harsh, the labor intensity is high, and the production efficiency is low. Previous studies have shown that thin coal seam mining finds it difficult to follow machines, does not have complete sets of equipment, has a low degree of automation, and has difficult system co-control, which easily causes production safety accidents. In order to effectively solve the problems existing in thin coal seam mining, Binhu Coal Mine has established intelligent fully mechanized mining and actively explored automatic coal cutting, automatic support following, and intelligent control. The combination of an SAC electro-hydraulic control system and SAP pumping station control system has been applied in 16,108 intelligent fully mechanized coal mining faces, which realizes the automatic following of underground support and the control of adjacent support, partition support, and group operation; the automatic coal cutting of the shearer is realized by editing the automatic coal-cutting state of the shearer and adjusting the automatic parameters. A centralized control center is set up, which realizes the remote control and one-button start–stop of working face equipment. Through a comparative analysis of 16,108 intelligent fully mechanized mining faces and traditional fully mechanized mining faces, it is found that intelligent fully mechanized mining faces have obvious advantages in terms of equipment maintenance, equipment operation mode, and working face efficiency, which improve the equipment and technical mining level of thin coal seam. The application of intelligent mining in Binhu Coal Mine has a great and far-reaching impact on the development of thin coal seam mining technology in China. Full article
(This article belongs to the Special Issue Recent Advances in Optical Sensor for Mining)
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Figure 1

Figure 1
<p>Location map of working faces.</p> Full article ">Figure 2
<p>Composition of intelligent mining system for fully mechanized coal mining face in thin coal seam.</p> Full article ">Figure 3
<p>Structure Schematic Diagram of SAC Electro-hydraulic Control System for Hydraulic Support.</p> Full article ">Figure 4
<p>Schematic diagram of automatic coal cutting and video monitoring system of shearer.</p> Full article ">Figure 5
<p>One-button start–stop host screen.</p> Full article ">Figure 6
<p>Equipment Maintenance Mode Control Chart.</p> Full article ">Figure 7
<p>Control diagram of bracket operation mode.</p> Full article ">Figure 8
<p>Comparison of working face efficiency.</p> Full article ">
14 pages, 9210 KiB  
Article
A New Approach to Studying the Mechanical Characteristics of the Anchoring–Grouting System in Broken Surrounding Rock
by Lei Wang and Wei Lu
Sensors 2023, 23(21), 8931; https://doi.org/10.3390/s23218931 - 2 Nov 2023
Viewed by 889
Abstract
With the increasing depletion of shallow coal resources, deep roadway excavation has become the main direction in the development of coal mining. Due to geological conditions including high stress and extremely broken rock, disasters such as squeezing, bulging, and swelling are widely observed. [...] Read more.
With the increasing depletion of shallow coal resources, deep roadway excavation has become the main direction in the development of coal mining. Due to geological conditions including high stress and extremely broken rock, disasters such as squeezing, bulging, and swelling are widely observed. The anchoring–grouting support method is one of the most effective methods of surrounding rock reinforcement. To study the mechanical characteristics of the anchoring–grouting system in broken surrounding rock, laboratory tests considering the water–cement ratio and preload were carried out. The research results show that the internal force of support and the deformation of the support surface have close relationships with the bearing stages of the anchoring–grouting system. The optimal water–cement ratio and a higher preload can improve the cooperative bearing characteristics of surrounding rock and its support, which is of great significance for enhancing the strength of surrounding rock and reducing roadway deformation. The research results can provide a reference for anchoring–grouting support design in deep roadway excavation. Full article
(This article belongs to the Special Issue Recent Advances in Optical Sensor for Mining)
►▼ Show Figures

Figure 1

Figure 1
<p>Failure phenomenon of broken surrounding rock in deep coal mine roadway.</p> Full article ">Figure 2
<p>Bearing structure model and test scheme of anchoring–grouting system.</p> Full article ">Figure 3
<p>The test scheme of the anchoring–grouting system.</p> Full article ">Figure 4
<p>Stress–strain curve of anchoring–grouting system with different water–cement ratios. (<b>a</b>) Stress–strain curve. (<b>b</b>) Compressive strength histogram.</p> Full article ">Figure 5
<p>Failure modes of anchoring–grouting system with different water–cement ratios. (<b>a</b>) Water–cement ratio: 0.4. (<b>b</b>) Water–cement ratio: 0.5. (<b>c</b>) Water–cement ratio: 0.8.</p> Full article ">Figure 6
<p>Relationship between the monitored loads/force and the vertical displacement of the anchoring–grouting system and the support members. (<b>a</b>) Water–cement ratio: 0.4. (<b>b</b>) Water–cement ratio: 0.5. (<b>c</b>) Water–cement ratio: 0.8. (<b>d</b>) Stress comparison curve of supporting members.</p> Full article ">Figure 7
<p>The cooperative bearing curve of the anchoring–grouting system with different water–cement ratios.</p> Full article ">Figure 8
<p>Stress and displacement curve of support. (<b>a</b>) Water–cement ratio: 0.4. (<b>b</b>) Water–cement ratio: 0.5. (<b>c</b>) Water–cement ratio: 0.8. (<b>d</b>) Stress comparison curve of supporting components.</p> Full article ">Figure 9
<p>Failure modes of anchor–grouting system specimens.</p> Full article ">Figure 10
<p>Coordinated deformation coefficient curve of the anchoring–grouting system with different water–cement ratios.</p> Full article ">Figure 11
<p>Stress–strain curve of the anchoring–grouting system with different preloads. (<b>a</b>) Stress–strain curve. (<b>b</b>) Compressive strength curve.</p> Full article ">Figure 12
<p>Failure modes of anchoring–grouting system with different preloads. (<b>a</b>) Preload: 0 kN. (<b>b</b>) Preload: 1 kN. (<b>c</b>) Preload: 1.75 kN.</p> Full article ">Figure 13
<p>Bearing capacity of anchoring–grouting system and stress curve of supporting component. (<b>a</b>) Preload: 1 kN. (<b>b</b>) Preload: 1.75 kN. (<b>c</b>) Comparison curve.</p> Full article ">Figure 14
<p>The cooperative bearing curve of the anchoring–grouting system with different preloads.</p> Full article ">Figure 15
<p>Displacement and force curve of supporting members. (<b>a</b>) Preload: 1 kN. (<b>b</b>) Preload: 1.75 kN. (<b>c</b>) Comparison curve.</p> Full article ">Figure 16
<p>Coordinated deformation coefficient curve of anchoring–grouting system with different preloads.</p> Full article ">
20 pages, 6897 KiB  
Article
Extraction and Application of Hydraulic Support Safety Valve Characteristic Parameters Based on Roof Pressure Data
by Keke Xing, Jingyi Cheng, Zhijun Wan, Xin Sun, Wanzi Yan, Jiakun Lv and Minti Xue
Sensors 2023, 23(21), 8853; https://doi.org/10.3390/s23218853 - 31 Oct 2023
Cited by 2 | Viewed by 1237
Abstract
The safety valves of powered supports control the maximum working resistance, and their statuses must be known to ensure the safety of both the support and the overlying strata. However, the inspection of powered support valves involves manual or semiautomated operations, the costs [...] Read more.
The safety valves of powered supports control the maximum working resistance, and their statuses must be known to ensure the safety of both the support and the overlying strata. However, the inspection of powered support valves involves manual or semiautomated operations, the costs of which are high. In this study, an extreme point extraction method was developed for the determination of the characteristic parameters of safety valves using roof pressure data, and a safety valve state monitoring module was constructed. Using the longwall face of 0116306 with top coal caving in the Mindong Mine as an example, the characteristic parameters of the safety valves were extracted, including the peak, reseating, and blowdown pressures, as well as the recovery and unloading durations. The results of the field tests showed the following: (1) The amplitude threshold method based on extreme points can be used to accurately extract characteristic parameters, and the distribution of the characteristic parameters of the safety valves follows either a Gaussian or an exponential distribution. (2) The mining pressure analysis results, derived from the characteristic parameters, closely align with the in situ mining pressure observations. This method can be used for the online monitoring of safety valve conditions, increasing the operational efficiency and quality of safety valve inspections. Full article
(This article belongs to the Special Issue Recent Advances in Optical Sensor for Mining)
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<p>Schematic diagram of hydraulic system and structural model of safety valve: (<b>a</b>) hydraulic system for support columns; (<b>b</b>) simplified structural diagram of fad320/40 spring-loaded safety valve.</p> Full article ">Figure 2
<p>AMESim simulation model: powered support leg (with safety valve).</p> Full article ">Figure 3
<p>Pressure curve of the lower leg cavity.</p> Full article ">Figure 4
<p>Static characteristic curve for fad320/40 safety valve.</p> Full article ">Figure 5
<p>Dynamic characteristic curve of safety valve column.</p> Full article ">Figure 6
<p>Simulation model of safety valve.</p> Full article ">Figure 7
<p>Dynamic characteristic curve of safety valve in AMESim.</p> Full article ">Figure 8
<p>Reason for feature parameter extraction for safety valve.</p> Full article ">Figure 9
<p>Flow chart of method of extracting characteristic safety valve parameters.</p> Full article ">Figure 10
<p>Extraction of pressure signal for safety valve: (<b>a</b>) roof raw pressure data cleaning and preprocessing; (<b>b</b>) extraction and correction of extreme points; (<b>c</b>) extreme point extraction based on magnitude thresholding method; (<b>d</b>) recorrection of waveforms and extraction of characteristic parameters.</p> Full article ">Figure 10 Cont.
<p>Extraction of pressure signal for safety valve: (<b>a</b>) roof raw pressure data cleaning and preprocessing; (<b>b</b>) extraction and correction of extreme points; (<b>c</b>) extreme point extraction based on magnitude thresholding method; (<b>d</b>) recorrection of waveforms and extraction of characteristic parameters.</p> Full article ">Figure 11
<p>Geological and mining conditions: (<b>a</b>) partial stratigraphic sequence; (<b>b</b>) layout of panel 0116<sup>3</sup>06.</p> Full article ">Figure 12
<p>Rock structure characteristics of panel 0116<sup>3</sup>06: (<b>a</b>) inclination and (<b>b</b>) strike of panel.</p> Full article ">Figure 13
<p>Statistics of safety valve opening frequency: (<b>a</b>) number of openings; (<b>b</b>) number of opening cycles.</p> Full article ">Figure 14
<p>Inter-relationships among the characteristic parameters of a single safety valve: relationship between the characteristic parameters and (<b>a</b>) safety valve openings and (<b>b</b>) time.</p> Full article ">Figure 15
<p>Overall distribution status of characteristic parameters for safety valves: (<b>a</b>) peak pressure, (<b>b</b>) reseating pressure, (<b>c</b>) blowdown pressure, (<b>d</b>) pressure recovery time, and (<b>e</b>) unloading time.</p> Full article ">Figure 16
<p>The overall display of safety valve status.</p> Full article ">Figure 17
<p>Statistics of safety valve opening rate: (<b>a</b>) number of safety valve opening cycles; (<b>b</b>) peak pressure.</p> Full article ">
15 pages, 4875 KiB  
Article
Research on the Three-Machines Perception System and Information Fusion Technology for Intelligent Work Faces
by Haotian Feng, Xinqiu Fang, Ningning Chen, Yang Song, Minfu Liang, Gang Wu and Xinyuan Zhang
Sensors 2023, 23(18), 7956; https://doi.org/10.3390/s23187956 - 18 Sep 2023
Cited by 2 | Viewed by 1205
Abstract
The foundation of intelligent collaborative control of a shearer, scraper conveyor, and hydraulic support (three-machines) is to achieve the precise perception of the status of the three-machines and the full integration of information between the equipment. In order to solve the problems of [...] Read more.
The foundation of intelligent collaborative control of a shearer, scraper conveyor, and hydraulic support (three-machines) is to achieve the precise perception of the status of the three-machines and the full integration of information between the equipment. In order to solve the problems of information isolation and non-flow, independence between equipment, and weak cooperation of three-machines due to an insufficient fusion of perception data, a fusion method of the equipment’s state perception system on the intelligent working surface was proposed. Firstly, an intelligent perception system for the state of the three-machines in the working face was established based on fiber optic sensing technology and inertial navigation technology. Then, the datum coordinate system is created on the working surface to uniformly describe the status of the three-machines and the spatial position relationship between the three-machines is established using a scraper conveyor as a bridge so that the three-machines become a mutually restricted and collaborative equipment system. Finally, an indoor test was carried out to verify the relational model of the spatial position of the three-machines. The results indicate that the intelligent working face three-machines perception system based on fiber optic sensing technology and inertial navigation technology can achieve the fusion of monitoring data and unified expression of equipment status. The research results provide an important reference for building an intelligent perception, intelligent decision-making, and automatic execution system for coal mines. Full article
(This article belongs to the Special Issue Recent Advances in Optical Sensor for Mining)
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<p>Establishment of d-system and three-machines state sensing system for working face.</p> Full article ">Figure 2
<p>Schematic diagram and product diagram of FBG 3D curvature sensor (<b>a</b>) structural schematic diagram of FBG 3D curvature sensor; (<b>b</b>) FBG 3D curvature sensor products.</p> Full article ">Figure 3
<p>Hydraulic support status sensing sensor. (<b>a</b>) FBG inclination sensor; (<b>b</b>) FBG pressure sensor.</p> Full article ">Figure 4
<p>Position and attitude perception of shearer based on IMU.</p> Full article ">Figure 5
<p>Layout diagram of straightness perception system for scraper conveyor.</p> Full article ">Figure 6
<p>Schematic diagram of bending section of scraper conveyor.</p> Full article ">Figure 7
<p>Schematic diagram of position and attitude perception system for hydraulic support.</p> Full article ">Figure 8
<p>Schematic diagram of the position relationship between hydraulic support and FBG.</p> Full article ">Figure 9
<p>Description of shearer status information.</p> Full article ">Figure 10
<p>Diagram of the position relationship between shearer and hydraulic support.</p> Full article ">Figure 11
<p>Scraper conveyor under straight line conditions.</p> Full article ">Figure 12
<p>Scraper conveyor under bending conditions.</p> Full article ">
18 pages, 9991 KiB  
Article
Preliminary Design and On-Site Testing Methodology of Roof-Cutting for Entry Retaining in Underground Coal Mine
by Ying Chen, Zikai Zhang, Shiji Bao, Hongtao Yang, Mingzhe Shi and Chen Cao
Sensors 2023, 23(14), 6391; https://doi.org/10.3390/s23146391 - 14 Jul 2023
Viewed by 1146
Abstract
Entry retaining via roof cutting is a new longwall mining method that has emerged in recent years, and is characterized by high resource utilization and environmental friendliness. Due to the complexity of this method, a field study is commonly employed for process optimization. [...] Read more.
Entry retaining via roof cutting is a new longwall mining method that has emerged in recent years, and is characterized by high resource utilization and environmental friendliness. Due to the complexity of this method, a field study is commonly employed for process optimization. Roof blasting is a key operation for retaining the entry, and the current practice involves dynamically adjusting blasting parameters through on-site testing and postblasting monitoring. However, the existing literature lacks detailed descriptions of blasting operations, making it difficult for field engineers to replicate the results. In this study, based on a roof cutting project for entry retaining, a preliminary design of blasting parameters is made based on theories and on-site geological conditions. The on-site test methods and equipment for roof-cutting blasting are described in detail, and the fractural patterns under different blasting parameters are analyzed. After the retreat of the working face, the state of roof caving in the goaf is analyzed based on monitoring data, and the effectiveness of top cutting is evaluated through reverse analysis, leading to dynamic adjustments of the blasting parameters. This research provides a reproducible construction method for roof-cutting operations and establishes the relationship between blasting parameters and post-mining monitoring data. It contributes to the development of fundamental theories and systematic technical systems for entry retaining via roof cutting, offering high-quality case studies for similar geological engineering projects. Full article
(This article belongs to the Special Issue Recent Advances in Optical Sensor for Mining)
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<p>The 20221 working face and retaining entry.</p> Full article ">Figure 2
<p>ZKXG100 mine-drilling imaging trajectory detection device; left: host; middle: 24 mm sensor; right: depth detector.</p> Full article ">Figure 3
<p>Borehole images at 1500 m.</p> Full article ">Figure 4
<p>Overlying strata of the 20221 main gate.</p> Full article ">Figure 5
<p>Roof-cutting height design.</p> Full article ">Figure 6
<p>The surrounding rock in the process of entry retaining via roof cutting (A, B, C are the key blocks).</p> Full article ">Figure 7
<p>Blasting design model.</p> Full article ">Figure 8
<p>Single-hole blasting charge design (Unit: mm).</p> Full article ">Figure 9
<p>Hole collapse 159# (<b>top left</b>), 161# (<b>top right</b>), 244# (<b>bottom left</b>), and 243# (<b>bottom right</b>).</p> Full article ">Figure 9 Cont.
<p>Hole collapse 159# (<b>top left</b>), 161# (<b>top right</b>), 244# (<b>bottom left</b>), and 243# (<b>bottom right</b>).</p> Full article ">Figure 10
<p>Blasting structure and crack formation of boreholes (<b>a</b>) 149#, (<b>b</b>) 159#, and (<b>c</b>) 250#.</p> Full article ">Figure 11
<p>Borehole 160# conditions.</p> Full article ">Figure 12
<p>Blasting structure and crack formation of boreholes 244#, 243#, 62#, and 72# (Blue zone represents the stemming section, brown zone represents the charging section).</p> Full article ">Figure 13
<p>Comparison of fissure formation percentage for different boreholes.</p> Full article ">Figure 14
<p>Left: 62# fissure formation at 5.0 m; right: 72# fissure formation at 3.7 m.</p> Full article ">Figure 15
<p>Arrangement of monitoring station.</p> Full article ">Figure 16
<p>Hydraulic load of shields 4#, 13#, 67#, and 130#.</p> Full article ">

Review

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19 pages, 4770 KiB  
Review
Overview of Health-Monitoring Technology for Long-Distance Transportation Pipeline and Progress in DAS Technology Application
by Yuyi Wu, Lei Gao, Jing Chai, Zhi Li, Chenyang Ma, Fengqi Qiu, Qiang Yuan and Dingding Zhang
Sensors 2024, 24(2), 413; https://doi.org/10.3390/s24020413 - 10 Jan 2024
Cited by 6 | Viewed by 1882
Abstract
There are various health issues associated with the different stages of long-distance pipeline transportation. These issues pose potential risks to environmental pollution, resource waste, and the safety of human life and property. It is essential to have real-time knowledge of the overall health [...] Read more.
There are various health issues associated with the different stages of long-distance pipeline transportation. These issues pose potential risks to environmental pollution, resource waste, and the safety of human life and property. It is essential to have real-time knowledge of the overall health status of pipelines throughout their entire lifecycle. This article investigates various health-monitoring technologies for long-distance pipelines, providing references for addressing potential safety issues that may arise during long-term transportation. This review summarizes the factors and characteristics that affect pipeline health from the perspective of pipeline structure health. It introduces the principles of major pipeline health-monitoring technologies and their respective advantages and disadvantages. The review also focuses on the application of Distributed Acoustic Sensing (DAS) technology, specifically time and space continuous monitoring technology, in the field of pipeline structure health monitoring. This paper discusses the process of commercialization development of DAS technology, the main research progress in the experimental field, and the open research issues. DAS technology has broad application prospects in the field of long-distance transportation pipeline health monitoring. Full article
(This article belongs to the Special Issue Recent Advances in Optical Sensor for Mining)
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<p>Principle of Combining Closed Circuit Television (CCTV) and Sonar Technology to Monitor Pipelines.</p> Full article ">Figure 2
<p>Schematic diagram of 5G unmanned aerial vehicle-inspection architecture design for the Nanjing Zhenjiang section of the Sunan-refined oil pipeline.</p> Full article ">Figure 3
<p>Principle of Magnetic-Leakage Technology for Monitoring Pipeline Leakage.</p> Full article ">Figure 4
<p>Principle of Pipeline Leakage Monitoring and Location Based on Acoustic-Emission Technology.</p> Full article ">Figure 5
<p>Principle of distributed optical fiber-sensing monitoring.</p> Full article ">Figure 6
<p>DAS system working process.</p> Full article ">Figure 7
<p>DAS measurement principle.</p> Full article ">Figure 8
<p>Development history of DAS Technology.</p> Full article ">Figure 9
<p>Experimental device for sediment content based on DAS system.</p> Full article ">Figure 10
<p>Experimental device of distributed acoustic sensor based on flow-induced vibration.</p> Full article ">Figure 11
<p>Pipeline flow-monitoring system based on DAS and flow-induced vibration principle. (<b>a</b>) The Non-invasive online flow-monitoring experimental device based on DAS system and FIV. (<b>b</b>) Field trials. (<b>c</b>) Distributed flow-monitoring results of straight pipe sections.</p> Full article ">Figure 12
<p>Experimental device for flow-induced vibration of straight pipe.</p> Full article ">Figure 13
<p>Distributed optical fiber vibration-sensing experimental system for pipeline leakage monitoring.</p> Full article ">
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