基于案例推理的竖炉故障预报系统

Fault prediction system using case-based reasoning for shaft furnace status

为降低竖望炉焙烧过程的故障发生率,基于故障机理的分析,将过程参量预报与案例推理技术相集成,提出了竖炉焙烧过程的智能故障预报方法.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焙烧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.将所建立的故障预报系统成功应用于竖炉焙烧过程的生产实际中,故障发生率明显降低,取得了显著应用成效.

For reducing the fault ratio of shaft ore-roasting furnace, based on the analysis of the fault mechanism and combination of case-based reasoning （CBR） and variables prediction, an intelligent fault prediction approach is proposed for the shaft furnace roasting process. The prediction model of the process variables 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 are obtained. The proposed fault prediction system is successfully applied to the roasting process of a shaft furnace, the fault ratios during production process is decreased, and the proved benefit is achieved.