Coal Mine Safety Monitoring System Research Articles

Lora Based Coal Mine Safety Monitoring and Security System Using Smart Helmet

The process of removing coal from the earth is called coal mining. Coal is used as fuel by the steel and cement industries to extract iron from iron ore and to make cement. The category that includes the underground mining business includes all factors that require routine monitoring, including methane gas, high temperatures, fire accidents, and so on. The safe production level of coal mines is still low, and frequent tragedies there result in significant losses of life and property. It is crucial to keep an eye on the working conditions in coal mines since the variety of work done there and the complexity of the mine environment are the main causes of disasters. The coal mine based on LoRa Long-Range Wide Area Network (LoRaWAN) technology is used by the Safety Monitoring and Security System to improve security and safety in coal mines. This system incorporates cutting-edge sensors to continuously monitor the surrounding environment, gas concentrations, and worker welfare. LoRa enables seamless communication and quick identification of potential risks because to its long range capabilities.

Sound Identification Method for Gas and Coal Dust Explosions Based on MLP.

To solve the problems of backward gas and coal dust explosion alarm technology and single monitoring means in coal mines, and to improve the accuracy of gas and coal dust explosion identification in coal mines, a sound identification method for gas and coal dust explosions based on MLP in coal mines is proposed, and the distributions of the mean value of the short-time energy, zero crossing rate, spectral centroid, spectral spread, roll-off, 16-dimensional time-frequency features, MFCC, GFCC, short-time Fourier coefficients of gas explosion sound, coal dust sound, and other underground sounds were analyzed. In order to select the most suitable feature vector to characterize the sound signal, the best feature extraction model of the Relief algorithm was established, and the cross-entropy distribution of the MLP model trained with the different numbers of feature values was analyzed. In order to further optimize the feature value selection, the recognition results of the recognition models trained with the different numbers of sound feature values were compared, and the first 35-dimensional feature values were finally determined as the feature vector to characterize the sound signal. The feature vectors are input into the MLP to establish the sound recognition model of coal mine gas and coal dust explosion. An analysis of the feature extraction, optimal feature extraction, model training, and time consumption for model recognition during the model establishment process shows that the proposed algorithm has high computational efficiency and meets the requirement of the real-time coal mine safety monitoring and alarm system. From the results of recognition experiments, the sound recognition algorithm can distinguish each kind of sound involved in the experiments more accurately. The average recognition rate, recall rate, and accuracy rate of the model can reach 95%, 95%, and 95.8%, respectively, which is obviously better than the comparison algorithm and can meet the requirements of coal mine gas and coal dust explosion sensing and alarming.

Internet of Things Based Coal Mine Safety Monitoring System

Internet of Things Based Coal Mine Safety Monitoring System

Design and Implementation of Coal Mine Safety Monitoring System Based on GIS

In order to further improve the safety production management capacity of coal enterprises and minimize the rate of coal mine safety accidents, based on the purpose of improving safety production efficiency, this paper constructs a coal mine safety detection system based on GIS technology. At the same time, on the basis of fixed-point positioning and remote monitoring at the PC end, this paper implements the whole process detection and monitoring of coal mine safety production with the help of mobile terminal intelligent system. The system software uses Java language to code it. In the first round of test, one function omission, three serious system errors, 14 general errors, and 21 minor errors are found. The accident passing rate of the second test is greater than 99.88%, and all performance indexes of the system meet the performance requirements, which proves the feasibility of the system.

Present Situation and Development of Intelligent Coal Mine Safety Monitoring System

Present Situation and Development of Intelligent Coal Mine Safety Monitoring System

A Network Communication Frequency Routing Protocol of Coal Mine Safety Monitoring System Based on Wireless Narrowband Data Communication Network

In recent years, the coal industry has been expanding the scale and output of coal mining in China, yet the safety in production is faced with great challenge. Communication system in coal mines plays a significant role in production safety, and wireless narrowband data communication network technology is a new-type advantageous measure to construct wireless network security monitoring system for coal mining. Here, a coal mine monitoring signal transmission method based on bus technology and wireless data transmission technology suitable for coal mines is proposed. The corresponding monitoring system is designed, and the system is able to collect the gas concentration and mechanical operation status of the mines. Wireless sensor network nodes are used for the RF communication module. The communication performance of this system in coal and rock medium and the roadway along the goaf is simulated, and the quantitative relationship between the attenuation coefficient of different communication frequencies and the effective communication distance is obtained. Hence, the results show that the protocol has good performance in terms of route establishment, maintenance, and data transmission hop count for the positioning of coal mine employees.

Design of Coal Mine Safety Monitoring System Based on Multi-network Fusion and Emergency Linkage

Design of Coal Mine Safety Monitoring System Based on Multi-network Fusion and Emergency Linkage

Research on Massive Data Processing System of Coal Mine Safety Monitoring Based on Computer Technology

Production safety is the most important problem in coal mine production, which requires us to monitor and monitor the coal mine production process in real time. Therefore, under the computer technology, we can carry out multi-sensor acquisition, including gas concentration, carbon monoxide content, environmental pressure, humidity, etc., which can collect and monitor the underground operation in real time. Through computer technology, coal mining enterprises can monitor and control safety production in real time. Through the sensor, we can monitor the working state of equipment, such as circulating fan, high-pressure water pump, winch and motor, which can deal with a variety of abnormal production problems, such as over-voltage, power loss of current, lack of phase. With the application of various sensors, the information system will collect more and more data, which will lead to great challenges for coal mine safety monitoring system. Therefore, we must enhance the data processing system, which will better coordinate the data interaction of various subsystems. Through the data processing system, we can improve the efficiency of coal mine safety monitoring, which will ensure the smooth operation of coal mining enterprises. Firstly, this paper analyzes the coal mine safety monitoring system. Then, this paper analyzes the source of the data. Finally, this paper analyzes the processing method.

Design of networking platform for coal mine safety monitoring system based on localization

Design of networking platform for coal mine safety monitoring system based on localization

IoT Based coal mine safety monitoring and controlling

Many coal miners are concerned about their safety in the workplace. Within subsurface mines, poor ventilation exposes workers to toxic gases, heat, and dust, which can lead to sickness, injury, and death. With the aid of an ARM controller, this work proposes a concept for an Internet of things (IoT), wireless sensor network (WSN) that can track temperature, humidity, and gas in an underground mine. This device uses a low-power, less-cost Arduino UNO, Node MCU, DHT11 sensor, gas sensor, fire sensor to detect fire and send an alarm, and LDR to detect light depending on light levels. Conventional coal mineshaft observing frameworks are regularly wired organization frameworks that assume a significant part in guaranteeing coal mineshaft security. With the ceaseless extension of mining zones and profundity in coal mineshafts, numerous laneways have become visually impaired territories with various secret risks. Furthermore, laying cables, which is costly and time-consuming, is inconvenient. To address the issues, a coal mine safety monitoring system based on a wireless sensor network and the Internet of Things, which can increase the monitoring level is created. Many micro-sensor nodes with small volumes and low costs make up an IoT and wireless sensor network.

Safety Monitoring System in Coal Mine Using IoT

The key abstract is to use IoT to incorporate a coal mine safety monitoring system. The extraction of coal from the field is known as coal mining. Coal is used as a fuel in the steel and cement industry to extract iron from ore and to manufacture cement. Every parameter in the underground mining industry must be controlled on a regular basis, including methane gas, high temperatures, fire incidents, and so on. The level of safety in coal mines is still poor, resulting in fatalities. A coal mineshaft salvage action is profoundly perilous because of various elements. It is particularly risky for rescuers to enter a coal mineshaft burrow in a debacle without earlier attention to the climate on the grounds that ensuing blasts are probably going to happen at any second. It is along these lines basic to recognize unstable climate data like toxic gases and high temperatures, just as to direct a visual review of excavators caught in a fell passage through the imploded burrow. These information would aid rescuers in devising a strategy and equipping themselves to carry out the rescue operation defensively. This paper proposes a design for coal mines that will reduce the damage caused by a coal mine accident and allow for a more effective rescue operation.

Coal Mine Safety Monitoring and Alert System

The accidents in coal mines are increased day by day. There are numerous life losses of many skilled workers and laborers. There is no advent precaution measure to detect the alarming cause of the coal mine accidents and provide an alert system. Occupational accidents and occupational diseases are common in the mining. The most common causes of accidents in coal mining are firedamp and dust explosions, landslips, mine fires, and technical failures related to transport and mechanization. An analysis of occupational accidents in the consideration of social and economic factors reports that the real causes behind these accidents, which are said to happen inevitably due to technical deficiencies or failures. Thus an automated alarming coal mine accident detect ion system is employed to rescue and protect the workers from the hazards. This system incorporates the combined action of the temperature, pressure and gas sensor and IOT module to detect the temperature, pressure and atmosphere in the coal mine and log every data onto the cloud using data logging. Then these data are accepted by a admin controlled sever page through data acquisition. The data processing takes place at a server page and the alert is send to the device to glow the alarm and to the concerned officials and rescue stations for taking the prevention measures.

Construction of Harmful Gas Monitoring System in Coal Mine Working Environment

In order to improve the accuracy, sensitivity, reliability, stability and maintainability of the coal mine safety monitoring system, and enhance the coal mine safety guarantee ability, combined with the national coal mine environmental monitoring network construction scheme and the coal mine safety production and environmental monitoring management regulations, the coal mine safety production status and environmental data are analyzed. In order to improve the production efficiency and provide reliable guarantee for the safety production of coal mine, the monitoring system of harmful gas in the working site of coal mine roadway is established.

Coal Mine Safety Intelligent Monitoring Based on Wireless Sensor Network

Wireless Sensor Network (WSN) explores the use of the latest WSN technology in coal mine safety intelligent wireless monitoring, especially the three key technologies that need to solve the WSN wireless communication, transmission routing protocol and positioning algorithm for underground safety monitoring. The application of wireless sensor network in coal mine safety intelligent monitoring system is proposed, this paper discusses the principle and advantages of wireless sensor network and the design basis of wireless sensor network in intelligent monitoring system of coal mine safety. Based on the current situation and existing problems of the low level of intelligence of coal mine safety monitoring system, the design scheme and monitoring mechanism of coal mine safety intelligent monitoring system are proposed, and the feasibility of wireless sensor network in the application of coal mine safety intelligent monitoring system is discussed. Finally, the gateway proposed in this research was tested by laboratory simulation, and the results showed that the gateway designed in this research had good reliability and stability, and proposed a new solution for the design of coal mine safety monitoring system.

Coal Mine Gas Safety Evaluation Based on Adaptive Weighted Least Squares Support Vector Machine and Improved Dempster–Shafer Evidence Theory

Gas safety evaluation has always been vital for coal mine safety management. To enhance the accuracy of coal mine gas safety evaluation results, a new gas safety evaluation model is proposed based on the adaptive weighted least squares support vector machine (AWLS-SVM) and improved Dempster–Shafer (D-S) evidence theory. The AWLS-SVM is used to calculate the sensor value at the evaluation time, and the D-S evidence theory is used to evaluate the safety status. First, the sensor data of gas concentration, wind speed, dust, and temperature were obtained from the coal mine safety monitoring system, and the prediction results of sensor data are obtained using the AWLS-SVM; hence, the prediction results would be the input of the evaluation model. Second, because the basic probability assignment (BPA) function is the basis of D-S evidence theory calculation, the BPA function of each sensor is determined using the posterior probability modeling method, and the similarity is introduced for optimization. Then, regarding the problem of fusion failure in D-S evidence theory when fusing high-conflict evidence, using the idea of assigning weights, the importance of each evidence is allocated to weaken the effect of conflicting evidence on the evaluation results. To prevent the loss of the effective information of the original evidence followed by modifying the evidence source, a conflict allocation coefficient is introduced based on fusion rules. Ultimately, taking Qing Gang Ping coal mine located in Shaanxi province as the study area, a gas safety evaluation example analysis is performed for the assessment model developed in this paper. The results indicate that the similarity measures can effectively eliminate high-conflict evidence sources. Moreover, the accuracy of D-S evidence theory based on enhanced fusion rules is improved compared to the D-S evidence theory in terms of the modified evidence sources and the original D-S evidence theory. Since more sensors are fused, the evaluation results have higher accuracy. Furthermore, the multisensor data evaluation results are enhanced compared to the single sensor evaluation outcomes.

Top corner gas concentration prediction using t‐distributed Stochastic Neighbor Embedding and Support Vector Regression algorithms

SummaryThe excess of gas concentration in the top corner of coal working face has always been the main factor restricting the safe productivity of coal mines. Therefore, the rapid and accurate prediction of top corner gas concentration is an effective method to prevent gas disasters. At the same time, the development of the Internet of things has made the gas monitoring data collected by the coal mine safety monitoring system exhibit nonlinear big data characteristics. In order to mine the characteristic data related to the gas concentration of the top corner from a high‐dimensional and nonlinear monitoring datasets, a model that integrates the t‐distributed Stochastic Neighbor Embedding algorithm (t‐SNE) and the Support Vector Regression (SVR) algorithm to predict the gas concentration of the top corner on the coal working face is proposed. First, the multidimensional monitoring data are nonlinearly dimension‐reduced by t‐SNE algorithm, which enabled the spatial feature data of the monitoring data to be extracted. After that, the SVR algorithm was used to construct the nonlinear regression model between the spatial feature data and the actual gas concentration of the top corner to predict the gas concentration of the top corner. The experimental results show that the predictive model based on t‐SNE and SVR was better than the multiple linear regression, SVR, Principal Components Analysis (PCA) + SVR. The results show the model based on t‐SNE and SVR was more stable and could provide more accurate predictions, anomaly sensitivity, and the fitness is 0.55628405, which can better fit the actual gas concentration of the top corner.

ZIGBEE BASED MINE SAFETY MONITORING SYSTEM WITH GSM

In this work, a safe Coal Mine Monitoring system which replaces the traditional coal mine monitoring systems which tend to be wired network systems. This play an important role in coal mine safe production. With continuous enlarging of exploiting areas and extension of depth in coal mine, many laneways become monitoring blind areas, where are lots of hidden dangers. Moreover, it is inconvenient to lay cables which are expensive and consume time. In order to solve the problems, we designed a coal mine safety monitoring system based on wireless sensor network, which can improve the level of monitoring production safety and reduce accident in the coal mine. Zigbee technology provides a direction for scientists who commit to solve the safety monitoring problems of coal mine. The purpose of this study is to propose a solution suitable to mine wireless communication, safety monitoring, give a proof to the further study.

The Wireless Sensor Networks Applied in the Coal Mine Safety Intelligent Monitoring System

In order to reduce or avoid the coal mine accidents, this paper proposes the application of wireless sensor networks (WSN) in the coal mine safety intelligent monitoring system, and discusses the principle and advantages of wireless sensor networks. According to the current situation of low level of intellectualization in coal mine safety monitoring system and the existing problems, this paper proposes the designing proposal and the monitoring mechanism of the coal mine safety intelligent monitoring system, discusses the feasibility, the method and the way of the application of the wireless sensor networks in the coal mine safety intelligent monitoring system.

An Analysis of the Existing Problems of China's Coal Mine Safety Monitoring and Control System

Coal Mine Safety Monitoring System is an important material guarantee for Coal Mine Safety Production. At present,most China's coal mines have established safety monitoring system, but in the use of safety monitoring system we've found many problems. In order to solve these problems ,our paper put forward some practical solutions. [1].

Coal Mine Safety Monitoring System Based on ZigBee

A methane monitoring system, consisting of methane sensor nodes and controllers with ZigBee networks between them, has been investigated for coal mine safety. The nodes were in comparatively flexible position. While not working, the nodes were put into hibernation so as to low consumption. While working, the sensor KGS-20 detected methane and sent voltage to CC2430 to carry on A/D conversion. After data processing by CC2430, the node would alarm and display and transmit data to its controller. The controller transmitted data to monitoring station by CAN bus. In experiment a sensor node was put in methane with eight different concentrations and the results were shown in LCD. The results show that all errors are all less than 0.05% and its average error is 0.023%. The errors are accordance with the rules for error range in coal mine.