Bayesian Network Structure and Parameter Analysis for Production Risk in Power Enterprises
Ensuring the safety of production processes is crucial for a company's continued success.This study introduces a Bayesian Network theory-based method to identify critical factors in production process risks and extract expert risk management insights.It constructs a warning indicator framework with 3 primary,12 secondary,and 34 tertiary indicators.Utilizing the K2 algorithm,this method enhances the learning of structures and parameters within the Bayesian Network,leading to a comprehensive production process risk warning model.Applied in a case study,this model enables detailed risk assessment,sensitivity analysis,and causal chain exploration for a specific enterprise.The results confirm the method's effectiveness in building Bayesian networks,identifying essential risk factors,and extracting expert insights,thus providing innovative strategies and tools for the dynamic monitoring and continuous enhancement of risk management practices in production processes.
Bayesian NetworkPower EnterpriseProduction Process Risk
万方数据
维普
