真空精炼系统故障诊断的模糊聚类分析  

Fuzzy clustering fault diagnosis of vacuum metallurgy system

作　　者：王庆[1] 巴德纯[1] 靳雨菲[2] 王晓冬[1] 陈锦松[2] 

机构地区：[1]东北大学机械工程与自动化学院,辽宁沈阳110004 [2]上海宝钢集团公司

出　　处：《冶金自动化》2004年第4期1-4,共4页Metallurgical Industry Automation

基　　金：辽宁省科技基金重点资助课题 ( 9910 2 0 0 10 2 )

摘　　要：大型真空冶金系统 (DVMS)的故障点多、故障现象与故障原因间对应关系复杂 ,很难从抽气机理上来精确地进行故障诊断。DVMS工艺要求能对该设备进行快速、准确的故障诊断。为此 ,介绍了一种基于模糊聚类算法的智能诊断模型。该模型具有很强的自学习、自组织能力 ,适用于大型复杂真空系统的故障诊断。以RH KTB真空冶金系统的智能故障诊断为例 ,给出了真空冶金系统模糊故障诊断的实际过程。In large vacuum metallurgy system (DVMS),there are many possible fault types,and relationship between fault symptoms and causes is complicated.It is difficult to exactly find out fault according to air pumping mechanism.But DVMS process needs quick and exact fault diagnosis for equipment.An intelligent diagnosis model based on fuzzy clustering algorithm is put forward for vacuum metallurgy system.The model has very high self-learning and self-organizing ability,and is applicable to fault diagnosis of large and complicated vacuum system.Taking intelligent fault diagnosis of RH-KTB vacuum metallurgy system as example,actual procedure of fuzzy fault diagnosis is presented.Through example analysis,it is proved that the algorithm is effective for intelligent fault diagnosis of large and complicated vacuum metallurgy system.

关 键 词：真空冶金 真空精炼系统 故障诊断 模糊聚类分析 自学习 DVMS RH-OB法 RH-KTB法 RH-PB法 

分 类 号：TF769[冶金工程—钢铁冶金]