Risk assessment of gas explosion based on fuzzy Bayesian network improved by entropy weight method - Wen HAN; Zhaoyang YU; Fei LIU; Haoyou ZHAO (Meikuang Anquan)
In order to accurately and effectively evaluate the risk of gas explosion in coal mines, an improved fuzzy Bayesian network gas explosion risk assessment model based on entropy weight method is proposed. Firstly, the risk identification of gas explosion accident cases is carried out, and 18 main risk factors of gas explosion are extracted. Secondly, the fault tree model is established, and the corresponding Bayesian network model is established according to the mapping rules. In order to reduce the subjectivity of expert judgment, the combination weight obtained by entropy weight method combined with fuzzy theory is taken as the prior probability of Bayesian network, and then the risk of gas explosion in Songlin Coal Mine of Guizhou Province is evaluated by this model. The results show that the risk probability of gas explosion in Songlin Coal Mine in Guizhou is 25 %, and the risk level is general risk. Gas accumulation and coal spontaneous combustion are the main risk factors of gas explosion. Among them, ventilation resistance, gas outburst, explosion-proof equipment failure, illegal blasting, coal spontaneous combustion and other factors are the key causes of gas explosion, and the evaluation results are consistent with the actual situation.