Risk Analysis of a Coal Mine Ventilation System Based on Fuzzy Fault Tree Analysis and Multistate Bayesian Networks

Abstract

The identification and control of key risk factors in the coal mine ventilation system is an important part and necessary means in preventing a series of accidents such as gas accumulation, gas outburst, and gas explosion. Risk assessment plays a positive role in eliminating the risks of the ventilation system. To accurately quantify the risks of the coal mine ventilation system and its indicators, this study proposes a new method for analyzing the risks of the coal mine ventilation system by integrating fault tree analysis and fuzzy multistate Bayesian networks. Firstly, a risk indicator system is constructed from the four dimensions of human, machine, environment, and management. Secondly, the risk factors are classified into multiple states, and a fuzzy multistate Bayesian network model is constructed. Finally, the method is validated using the H coal mine as a case study. The results showed that the probability of the coal mine ventilation system being in a high-risk state is 29%. Among the risk factors, work experience and adequacy of safety training are the main causes, and the sensitivity value of safety investment is the highest. The prevention and control of key factors can significantly reduce the risk. Furthermore, through a comparative analysis of multistate and two-state Bayesian networks, it was found that the multistate Bayesian network can better represent the actual risk state. The findings reveal the critical risk factors that require focused attention in coal mine ventilation systems to enhance system safety. These insights provide managers with actionable prevention and control strategies, along with theoretical support, to help effectively reduce the occurrence of coal mine accidents.