Notice of Retraction: Coal mine safety management system based on the chart analysis method

Author(s):  
Guo-xun Jing ◽  
Zhen-wei Duan ◽  
Chuang-qi Li ◽  
Sheng-ming Xu
2013 ◽  
Vol 734-737 ◽  
pp. 3030-3033 ◽  
Author(s):  
Shu Zhen Li ◽  
Hui Huang

In order to solve the three JiaoHe coal mine safety production the problems existing in the management and detailed analysis of the safety of this ore information management status, determine the safety information management system design goal, make the system design of the guiding ideology, the advanced nature, adaptability, according to the developmental and reliability of principles in the design, the management system has strong data processing ability can provide the scientific decision of leaders at all levels of the data support, advanced and reliable information means can help enterprise further improve safety management work process optimization and realize the goal of prevention, so as to reduce the number of accidents, ensure the safety of the QuanKuang production.


2021 ◽  
Vol 15 (2) ◽  
Author(s):  
Dian Palupi Restuputri ◽  
M Syahban Giraldi ◽  
Shanty Kusuma Dewi ◽  
Ilyas Masudin ◽  
Uci Yuliati

This article aims to measure the application of occupational safety and health using Cooper's Reciprocal Safety Culture Model and Confirmatory Factor Analysis method.  The objective function of this article is to find out the aspects of safety culture that have been implemented by companies. A questionnaire was circulated to staff on the company's production floor as part of this study. The results of the questionnaire recapitulation were then analyzed using the confirmatory factor analysis method. Based on the score calculation results and the category determination build on the questionnaire scores on each dimension of the safety culture applied to the Steel Company, the safety climate value of 55.58 is obtained, which is on a 'quite good' scale. The safety behaviour value of 44, 89 is included on a 'quite good' scale, the safety management system value of 22.04 is on a 'poor' scale, and the safety culture value of 40.83 is on the 'quite good' scale. With these results, it is essential to make improvements to the safety culture in the company, especially in the dimensions of the safety management system, which is on the 'quite good' scale.


2011 ◽  
Vol 58-60 ◽  
pp. 2564-2569
Author(s):  
Li Xia Qi ◽  
Xue Yang

Coal mine safety production is of great significance in China. Modern information management and network technology are used to establish a coal-based early warning emergency command information system. The system provides a fast and effective digital platform for the mine safety management and emergency response decision-making. The system's functional framework, operation process and some key algorithm of the system were introduced in the paper. The technology of System construction has also been mentioned.


2019 ◽  
Vol 9 (13) ◽  
pp. 2639 ◽  
Author(s):  
Jieun Baek ◽  
Yosoon Choi

Information communication technology (ICT)-based mine safety management systems are being introduced at numerous mining sites to track the location of equipment and workers in real time and monitor environmental changes. This paper presents the results of a case study in which the big data created by an ICT-based mine safety management system are used for simulating truck haulage operations. An underground limestone mine located in Danyang, South Korea was studied, and the data generated over three months, from October 1 to December 31, 2018, were analyzed. Truck tag packet data recognized by relays were extracted and analyzed to calculate the averages and standard deviations of the truck travel times of each mine segment. A discrete event simulation program that simulates truck haulage operations in the study area was developed. Haulage times, the number of haulage operations, production output, and truck delay times were predicted, and results were compared with the actual operation results that were obtained on January 2 and 9, 2019. The difference between the predicted and actual results for the total amount of loaded ore was 30 tons for January 2 and 0 tons for January 9. The mean absolute error between the predicted and observed truck travel times was 0.13 min for January 2 and 0.14 min for January 9. The truck travel times that were measured differently according to the data aggregation period were set as temporal factors, and truck haulage simulations were performed. The results showed that more reliable simulation results were obtained as data accumulation time increased.


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