Smart safety early warning system of coal mine production based on WSNs

Chen, Kansong; Wang, Chenqi; Chen, Liangqing; Niu, Xinxin; Zhou, Yan; Wan, Jixiang

doi:10.1016/j.ssci.2020.104609

Cited by 36 publications

(15 citation statements)

References 13 publications

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“…The occurrence of mining disasters do not guarantee the safety of coal mine workers 10 . To minimize the occurrence of coal mine accidents, it is necessary to intervene in the process of coal mining through technology and management to predict accidents in advance 11 . To prevent safety associated accidents during coal mining, it is necessary to evaluate the factors and mechanisms that lead to coal mine safety accidents.…”

Section: Introductionmentioning

confidence: 99%

The effect of job satisfaction regulating workload on miners’ unsafe state

Chen

Zhao

et al. 2022

Sci Rep

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Miners’ unsafe behavior is the main cause of accidents in coal mines, and unsafe state have an important influence on unsafe behavior among miners. To minimize accidents from the source of accident chain, we evaluated the impact of workload on miners’ unsafe state. It is important for coal enterprises to monitor miners’ unsafe state and to prevent unsafe accidents. Workload is divided into two dimensions: work time and work demand. Meanwhile, we introduced job satisfaction as a moderating variable. Through empirical research methods, first-line employees from two coal mines in China were enrolled in the questionnaire survey. Regression analysis was used to verify the impact of workload and its various dimensions, job satisfaction, and miners’ unsafe state. We found that workload, work time and work demand have significant positive effects on miners’ unsafe state. Job satisfaction plays a moderating effect in the relationship between workload and miners’ unsafe state. To some extent, a higher job satisfaction was associated with reduced workload, reduced occurrence of miners’ unsafe state and minimal incidences of unsafe accidents. On this basis, measures were proposed to improve miners’ unsafe state in terms of workload and job satisfaction. This study informs the establishment of effective intervention measures to monitor miners’ unsafe state and is also beneficial to the improvement of coal mine safety.

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Section: Introductionmentioning

confidence: 99%

The effect of job satisfaction regulating workload on miners’ unsafe state

Chen

Zhao

et al. 2022

Sci Rep

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“…Numerous authors have developed disaster management models for generating early warnings. In 2020, Chen et al 13 developed a smart safety monitoring system for personal tracking in coal mines, underground operations, and tunnel engineering. In the proposed framework, the wireless sensor networks are displaced for predicting the accurate position of a person.…”

Section: Literature Surveymentioning

confidence: 99%

Internet of things‐fog computing‐based framework for smart disaster management

Aljumah

Kaur

Bhatia

et al. 2020

Trans Emerging Tel Tech

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Smart building incorporates the ubiquitous sensing ability of Internet of things (IoT) technology with the industrial infrastructure for automating decision‐making. Inspired by this, a novel technique of stochastic game net (SGN) has been proposed to minimize the tangible and intangible infrastructural losses due to disasters. Specifically, IoT technology is used for disaster management and control in a smart building based on SGN. In the proposed technique, every IoT sensor act as an individual player with prefixed action sets and strategies. Complete SGN of the IoT network in the smart building is formalized by collaborating SGN of an individual sensor. The model deployed in IoT building with different sensors detects disasters beforehand and generates early warning alerts to management units based on game‐theoretic decision‐making. Experimental evaluation on four challenging datasets, namely, disaster/accident sources, gas sensor drift, red cross smoke alarm, and oil spills show the effectiveness of a proposed framework in the smart building. The results are compared with several state‐of‐the‐art decision‐making techniques to measure the overall effectiveness of the proposed system. Based on simulations performed on NetLogo 5.3.1, enhanced results of performance parameters including temporal delay, statistical efficiency, reliability, and stability were registered. Moreover, mathematical analysis has been carried out to determine the overall performance of the presented technique.

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“…Aiming at the problem of low matching positioning accuracy such as MSD and MAD [9], the MPMD matching algorithm based on the feature vector product is used to improve positioning accuracy, and the results are better in error and pit noise. In view of the blind areas in the mining and monitoring of underground coal resources, document [10] proposes an improved DV-Hop positioning and monitoring system, which can accurately track personal operations, calculate the distance per jump of anchor nodes, and collect relevant operation safety data. To improve the positioning accuracy, ensure the safety of personnel and improve the production level underground in complex environment.…”

Section: Introductionmentioning

confidence: 99%

Research on Underground Chemical Gas Monitoring and Target Location Based on an Improved Moth Flame Algorithm

Chen

2021

Journal of Sensors

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The danger of downhole work is mainly due to the chemical toxic gases and flammable gases NO2, CO, SO2, H2S, CH4, CO2, etc. When the concentration reaches a certain value, it will produce very great harm. With the continuous development of sensor technology and communication technology, it is necessary to monitor the relevant geographic features below the ground. Because of the complex environmental parameters of the coal mine roadway and the interference caused by various electrical equipment, the transmission of mine electromagnetic signals will be affected, resulting in low positioning accuracy. However, the underground chemical gas leakage leads to the life of underground workers which cannot be guaranteed, so it is necessary to effectively monitor the concentration of chemical gas components in underground mines. In this paper, a moth flame algorithm based on optimized inertia weights is proposed. By continuously improving the local inertia weights, the global optimum is determined by using the change of inertia weights in the iterative process of the algorithm. By testing the convergence and optimal value of several algorithms under common test functions, IMFO can obtain the global optimal solution. Finally, the concentrations of chemical gases NO2, CO, SO2, H2S, CH4, and CO2 are monitored by setting specific areas to see if they reach the early warning values. Then, 16 coordinates in the region are used to predict the above method, and the IMFO algorithm can achieve the best prediction effect.

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Smart safety early warning system of coal mine production based on WSNs

Cited by 36 publications

References 13 publications

The effect of job satisfaction regulating workload on miners’ unsafe state

The effect of job satisfaction regulating workload on miners’ unsafe state

Internet of things‐fog computing‐based framework for smart disaster management

Research on Underground Chemical Gas Monitoring and Target Location Based on an Improved Moth Flame Algorithm

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