scholarly journals Mathematical Modelling using Gray Markov SCGM(1,1)c of Zambia’s Fatal Mining Accidents Between 2001 and 2015

2020 ◽  
Author(s):  
Sunday Mulenga ◽  
Webby Banda
Keyword(s):  
Human Error ◽  
Engineering Application ◽  
Mine Safety ◽  
Grey Prediction ◽  
Fatal Accidents ◽  
Stochastic Fluctuations ◽  
Mine Accidents ◽  
Double Exponential Smoothing ◽  
Safety Accident ◽  
Mine Accident

Most mine accidents are caused by human error. The effects of accidents either fatal or not are adverse and range from economical to social. In this paper, the amended Grey Markov model with double exponential smoothing has been used. Predicting fatal accidents will provide the basis of safety assessment and decision making and also help to plan for possible economic and social impacts generated by fatal accidents. The amended Grey Markov combines the advantages of the grey prediction model and the Markov chains and can, therefore, be used on data that is few, has little and stochastic fluctuations. The gray SCGM(1,1)c model is applied to imitate the development tendency of the mine safety accident, and adopt the amended model to improve prediction accuracy, while Markov prediction is used to predict the fluctuation along with the tendency. Finally, the model is applied to forecast the fatal mine accident deaths from 2001 to 2015 in Zambia, and, 2016 fatal mine accidents were predicted. The model predicted the fatal mine accidents results with a relative error of 0.06 and is classified as excellent in the precision test. The proposed model, therefore, possesses a stronger engineering application.

scholarly journals Application of Gray Markov SCGM1,1c Model to Prediction of Accidents Deaths in Coal Mining

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Jian-yi Lan ◽  
Ying Zhou
Keyword(s):  
Human Error ◽  
Engineering Application ◽  
Mine Safety ◽  
Random Fluctuation ◽  
New Model ◽  
Gray Prediction ◽  
Markov Chain Theory ◽  
Safety Accident ◽  
Mine Accident ◽  
Few Data

The prediction of mine accident is the basis of aviation safety assessment and decision making. Gray prediction is suitable for such kinds of system objects with few data, short time, and little fluctuation, and Markov chain theory is just suitable for forecasting stochastic fluctuating dynamic process. Analyzing the coal mine accident human error cause, combining the advantages of both Gray prediction and Markov theory, an amended Gray Markov SCGM1,1c model is proposed. The gray SCGM1,1c model is applied to imitate the development tendency of the mine safety accident, and adopt the amended model to improve prediction accuracy, while Markov prediction is used to predict the fluctuation along the tendency. Finally, the new model is applied to forecast the mine safety accident deaths from 1990 to 2010 in China, and, 2011–2014 coal accidents deaths were predicted. The results show that the new model not only discovers the trend of the mine human error accident death toll but also overcomes the random fluctuation of data affecting precision. It possesses stronger engineering application.

Prediction and Analysis of Safety Production Accident in Mining Based on the GM(1,1)

2014 ◽  
Vol 989-994 ◽  
pp. 3443-3446
Author(s):  
Chen Fang Jiang ◽  
Ke Peng Hou ◽  
Hua Fen Sun
Keyword(s):  
Production Management ◽  
Management Practices ◽  
Safety Management ◽  
High Accuracy ◽  
Mine Safety ◽  
Grey Theory ◽  
Procedure Code ◽  
Safety Production ◽  
Mine Accidents

According to the grey theory, in order to predict and prevent accident effectively, the paper built a grey model and forecast the mine accidents in china in 2013 based on the statistics of mine accidents happened in China during period from 2007 to 2012. MATLAB was used to write procedure code of GM (1, 1) and empirical verification follows. The prediction results show that if high accuracy goes with the precision of the calculable model, which could be used to provide the basis for decision making to the safety production management practices in China. This case study indicates that GM (1, 1) plays an important role in mine safety management.

Understanding Human Error in Mine Safety

2018 ◽  
Author(s):  
Geoff Simpson ◽  
Tim Horberry ◽  
Jim Joy
Keyword(s):  
Human Error ◽  
Mine Safety

scholarly journals Coal Mine Safety Monitoring and Alert System

2021 ◽  
pp. 200-206
Author(s):  
Aditi Nagrale ◽  
Roshani Wakodikar ◽  
Priti Nakade ◽  
Ketan Marothi ◽  
Kalyani Raut ◽  
...  
Keyword(s):  
Coal Mine ◽  
Mine Safety ◽  
Occupational Accidents ◽  
Prevention Measures ◽  
Data Logging ◽  
Skilled Workers ◽  
Alert System ◽  
Mine Fires ◽  
Using Data ◽  
Mine Accident

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.

Analysis on the Human Errors in Coal Mines Based on Ant Clustering Algorithm

2014 ◽  
Vol 484-485 ◽  
pp. 634-638
Author(s):  
Shuang Yue Liu ◽  
Zi Xin Fang
Keyword(s):  
Coal Mine ◽  
Human Error ◽  
Clustering Algorithm ◽  
Safety Management ◽  
Working Environment ◽  
Classification Model ◽  
Mine Safety ◽  
Mechanical Equipment ◽  
Physiological Factors ◽  
Human Errors

The human error mechanism in coal mine safety is analyzed specifically from safety psychological and physiological factors, workers quality, safety management, safety education, mechanical equipment, and working environment, and also a human error dominant factors classification model playing a great effect on the safety production of coal mine is established with the application of ant clustering algorithm. The experimental results show that management is the key in the human errors of coal mine.

Use of Accident Information in The Development of Mining Industry Training Programs

1981 ◽  
Vol 25 (1) ◽  
pp. 553-553
Author(s):  
J. L. Woodward ◽  
G. E. Adkins
Keyword(s):  
Training Programs ◽  
Mining Industry ◽  
Mine Safety ◽  
Mining Equipment ◽  
Contributing Factors ◽  
Fatal Accidents ◽  
Safety And Health ◽  
Serious Injury ◽  
Industry Training

The investigation of serious injury and fatal accidents in the mining industry is mandated by 30 CFR 50 under the Federal Mine Safety and Health Act of 1977. The information derived from investigations can be put to important use in formulating training programs. Analysis of accident reports can result in information that points out contributing factors to accidents which not only can be modified or eliminated via administrative and/or design controls, but which can be addressed through training. This paper describes the use of accident reports to determine the relative necessity for development of training programs for mobile mining equipment operators.

scholarly journals Study on the Relationship between Worker States and Unsafe Behaviours in Coal Mine Accidents Based on a Bayesian Networks Model

2019 ◽  
Vol 11 (18) ◽  
pp. 5021 ◽  
Author(s):  
Zhaobo Chen ◽  
Gangzhu Qiao ◽  
Jianchao Zeng
Keyword(s):  
Bayesian Networks ◽  
Coal Mine ◽  
Expert Knowledge ◽  
Assessment Method ◽  
Mine Safety ◽  
Contributory Factor ◽  
Fitness For Duty ◽  
Mine Accidents ◽  
The Relationship

Unsafe behaviours, such as violations of rules and procedures, are commonly identified as important causal factors in coal mine accidents. Meanwhile, a recurring conclusion of accident investigations is that worker states, such as mental fatigue, illness, physiological fatigue, etc., are important contributory factors to unsafe behaviour. In this article, we seek to provide a quantitative analysis on the relationship between the worker state and unsafe behaviours in coal mine accidents, based on a case study drawn from Chinese practice. Using Bayesian networks (BN), a graphical structure of the network was designed with the help of three experts from a coal mine safety bureau. In particular, we propose a verbal versus numerical fuzzy probability assessment method to elicit the conditional probability of the Bayesian network. The junction tree algorithm is further employed to accomplish this analysis. According to the BN established by expert knowledge, the results show that when the worker is in a poor state, the most vulnerable unsafe behaviour is violation, followed by decision-making error. Furthermore, insufficient experience may be the most significant contributory factor to unsafe behaviour, and poor fitness for duty may be the principal state that causes unsafe behaviours.

scholarly journals Feature Analysis and Comparison of Prediction Methods for Fire Accidents

2020 ◽  
Vol 10 (5) ◽  
pp. 707-712
Author(s):  
Jiyu Zheng ◽  
Lingbo Zhang ◽  
Jiankun Gong ◽  
Wenkun Wang
Keyword(s):  
Preventive Measures ◽  
Historical Data ◽  
Maximum Relative Error ◽  
Grey Theory ◽  
Grey Prediction ◽  
Production Safety ◽  
Property Loss ◽  
Safety Accident ◽  
Fire Accidents ◽  
In Fire

Fire is one of the most common production safety accident. The trend of fire can be mastered by analyzing the historical data. This paper explores the features of recent fires in China, predicts fire by two methods, namely, grey theory, and grey Markov theory, and compares the prediction results of the two methods. The results show that: the number of fires in China increased greatly in 2013; Since 2014, the number of fires, as well as the number of deaths, the number of injured, and property loss induced by fires were declining. The maximum relative error of grey prediction was 5.8%, and that of grey Markov prediction was 5%; grey theory is less accurate in fire prediction than grey Markov prediction. According to the causes and features of fires, several preventive measures were put forward. The research results provide insights into the prevention of fires and protection of production safety.

scholarly journals Study on the role of the accident statistics in the design of coal mine safety signs

2021 ◽  
Vol 69 (5) ◽  
pp. 143
Author(s):  
Jiang Wei ◽  
Xiang Yuan-chi
Keyword(s):  
Coal Mine ◽  
Mine Safety ◽  
Coal Mine Safety ◽  
Operation And Maintenance ◽  
Pressing Operation ◽  
Mine Accident ◽  
Important Basis ◽  
Accident Statistics ◽  
Illegal Action

Accident statistics is an important basis in designing coal mine safety signs. In this paper, we study the process and feasibility for accident statistics as the design basis of the coal mine enterprise safety signs. This paper comes into a conclusion of unsafe actions which cause the accident can be divided into two categories: illegal action and error action. Illegal action could be subdivided into habitual illegal action and accident illegal action, while error action could be subdivided action into skills, decisionmaking and physiological perceptual action. In this paper, we specifically analyze five coal mine accident cases as examples, in order to conclude unsafe actions and unsafe states in every case. Specifically, unsafe acts are: bolt pretightening does not reach the designated position, sitting on the belt, no remote operating point column, maintenance without power cuts and operating under pressure. In addition, these unsafe acts lead to relative unsafe states, which are insufficient bolt pressure, point column instability, machine charging electricity and hydraulic pipe under pressure? Finally, these unsafe states become causations of accidents. Based on statistical analysis, we found out illegal actions of coal mine accident cases and specifically designed five safety signs, which are ‘bolt must be preloaded in place’, ‘be careful of the column’, ‘ban to sit on belts’, ‘forbidden pressing operation’ and ‘maintenance must be power outages’.

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