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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 12801

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Special Issue Editors

Dr. Minfu Liang

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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

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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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Research

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34 pages, 14710 KiB

Open AccessArticle

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 772

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 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)

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15 pages, 4475 KiB

Open AccessArticle

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 798

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 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)

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13 pages, 4105 KiB

Open AccessArticle

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 1165

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 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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16 pages, 10403 KiB

Open AccessArticle

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 5 | Viewed by 957

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 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)

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14 pages, 3795 KiB

Open AccessArticle

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 4 | Viewed by 2171

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 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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14 pages, 9210 KiB

Open AccessArticle

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 842

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. 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)

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20 pages, 6897 KiB

Open AccessArticle

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 1 | Viewed by 1156

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 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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15 pages, 4875 KiB

Open AccessArticle

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 1110

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 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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18 pages, 9991 KiB

Open AccessArticle

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 1064

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. 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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Review

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19 pages, 4770 KiB

Open AccessReview

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 1691

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 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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