Risk Assessment of Coal Mine Gas Explosion Based on Fault Tree Analysis and Fuzzy Polymorphic Bayesian Network: A Case Study of Wangzhuang Coal Mine

The prevention and control of gas explosion accidents are important means to improving the level of coal mine safety, and risk assessment has a positive effect on eliminating the risk of gas explosions. Aiming at the shortcomings of current risk assessment methods in dynamic control, state expression and handling uncertainty, this study proposes a method combining fault tree analysis and fuzzy polymorphic Bayesian networks. The risk factors are divided into multiple states, the concept of accuracy is proposed to correct the subjectivity of fuzzy theory and Bayesian networks are relied on to calculate the risk probability and risk distribution in real time and to propose targeted prevention and control measures. The results show that the current risk probability of a gas explosion accident in Wangzhuang coal mine is as high as 35%, and among the risk factors, excessive ventilation resistance and spontaneous combustion of coal are sources of induced risk, and the sensitivity value of electric sparks is the largest, and the prevention and control of the key factors can significantly reduce the risk. This study can provide technical support to coal mine gas explosion risk management.

Automated summary

This study proposes a new method to assess and deal with the risk of coal mine gas explosions. By combining fault tree analysis and fuzzy polymorphic Bayesian networks, the risk probability and risk distribution can be calculated in real-time, and targeted prevention and control measures can be proposed. The study finds that excessive ventilation resistance, spontaneous combustion of coal, and electric sparks are the main risk factors of gas explosions, and controlling these key factors can significantly reduce the risk.