【作者】 李公法； 蒋国璋； 孔建益； 谢良喜；

【Author】 Gongfa Li,Guozhang Jiang,Jianyi Kong,Liangxi Xie College of Machinery and Automation,Wuhan University of Science and Technology,Hubei,430081,China

【机构】 武汉科技大学机械自动化学院；

【摘要】 为降低焦炉加热过程的故障发生率,基于故障机理的分析,将案例推理技术与神经网络相集成,提出了焦炉加热过程的智能故障诊断方法.基于神经网络的参量预报模型对不易在线连续测量但能反映故障征兆的关键工艺参数进行实时预报,在此基础上,采用案例推理技术对加热过程进行全面分析并给出一些典型故障发生的概率和操作指导将所建立的故障诊断系统成功应用于某焦化厂焦炉加热过程的生产实际中,故障发生率明显降低,取得了显著应用成效.更多还原

【Abstract】 For reducing the fault ratio of coke oven heating process,based on the analysis of the fault mechanism and combination of case-based reasoning(CBR)and neural networks,an intelligent fault diagnosis method is proposed for the coke oven heating process.The prediction model of the process variables based on neural networks performs to predict key technical parameters as the fault symptoms that is hard to measure online.The probability of the typical fault and their operation guidance with the help of case-based reasoning technology is obtained.The proposed fault diagnosis system is successfully applied to the coke oven heating process,the fault ratios during production process is decreased, and the proved benefit is achieved.更多还原