تحلیل زنجیره علت از حادثه انفجار گاز معدن زغال سنگ بر اساس مدل شبکه بیزی Cause-chain analysis of coal-mine gas explosion accident based on Bayesian network model

1 Introduction Although the accident rate of coalmines has dropped significantly in China, the safety situation is still severer than the major coal-producing countries in abroad. Most coalmines in the country are underground mines with complex geological conditions and many disasters types, which threaten the lives and safety of employees. It is very important to understand hazards and risks associate with the process; perform risk assessment to identify them and take proper actions to remove or minimize hazards and risks; or else a catastrophic accident may occur. A small mistake by an operator or a problem in the process system may escalate into a disastrous event as the process area congests with process equipment and piping systems, and has limited ventilation and accidents escape routes. Case histories showed that catastrophic society as have a significant effect on people, environment, and they involved fatalities and great financial loss [1]. In coalmine accident, gas explosion accident is the casualty with the largest number of casualties, and cause great economic loss at the same time. The vast majority of mine in China are gas mines, some of them are high-gas mines. According to the statistics of the State Administration of Coal Mine Safety (SACMS) of China, the death toll from gas accidents always accounts for more than a half ratio in Chinese coal mines [2]. Therefore, doing well the prevention and control of gas accidents has the most prominent significance in reducing coalmine casualties and losses. Aiming at the causes of gas explosion accidents in coalmine, many experts and scholars in the world have done some significant research. Zhou et al. [3] propose a causing model of gas explosion based on probability analysis. Yin et al. [4] found the main point and principle of coal mine explosion accident by statistical analysis. Sanmiquel et al. used Bayesian classifiers, decision trees among other data mining techniques to explore prevention measures for Spanish mining accidents [5]. Li divvied coal hazards into four categories and made a risk assessment based on the probability, loss and weight to find the key factors [6]. According to the specific behavior factors of coal miners, Paul [7] analyzed the causes of gas explosion accidents and built a model equation based on human behavior factors. Amyotte [8] analyzed and studied a large amount of gas explosion data and processed the data from the perspective of management, which pointed out that management defects are an important factor in the gas explosion accidents of coal mining; therefore, a management failure model is constructed. As one kind of probabilistic graphical model, approaches like Bayesian network have significant representational as well as computational advantages when treating large, highly complex systems [9]. Martin et al. used Bayesian network to identifying the causes that have the greatest bearing on accidents involving auxiliary equipment. It allow a causality model to be defined for workplace accidents in a more realistic way to a management model for labour risk prevention [10].