Cause-chain analysis of coal-mine gas explosion accident based on Bayesian network model

Abstract

In order to prevent coal mining gas explosion accidents, it is considerable to understand the conditions and probabilities of casing process. This paper studied the cause of coal mining accidents and proposed a cause inference model of coal mining gas explosion accident based on Bayesian network. Firstly, the nodes and their domain in Bayesian network are determined by cause analysis. Secondly, we construct the Bayesian network through the accident samples and determine the conditional probability, and then attempt to build our Bayesian accident model. Finally, a GeNIe simulation software is adopted to analyze and verify the feasibility and effectiveness of our constructed model, and obtain the cause chain of coalmine gas explosion accident. The research results show that the inadequate technical management in the management elements has a relatively large impact on the accident. Technical management is not in place will result in many physical factors such as ventilation, electrical and other anomalies, and further cause coalmine gas explosion accident. Therefore, technical management should be strengthen to raise safety awareness and reduce equipment system abnormalities, to reduce such accidents and the hazards caused by explosion accident.