48MW高炉鼓风机组群在线状态监测与故障诊断  

Online Condition Monitoring and Fault Diagnosis for Group of 48MW Blast Furnace Blowing Engines

作　　者：董振兴[1] 史定国[2] 张东山[2] 杨汝清[1] 

机构地区：[1]上海交通大学机器人研究所,上海200030 [2]华东理工大学化工机械研究所,上海200237

出　　处：《机械科学与技术》2002年第3期442-445,共4页Mechanical Science and Technology for Aerospace Engineering

摘　　要：48 MW高炉鼓风机组结构复杂 ,系统庞大 ,根据其已有的监测、控制系统的具体情况 ,利用 L ab Windows/CVI虚拟仪器开发平台 ,采用虚拟仪器技术 ,开发了具有远程监测诊断能力的 4 8MW高炉鼓风机组群在线状态监测与故障诊断系统。实现了 Bently振动监测系统、μXL 集散控制系统和 Windows NT计算机网络系统的多复杂异构系统的信息集成。采用分层分类诊断策略 ,提出了一种基于产生式规则、事例、模糊诊断、神经网络集成模式的多参数综合智能故障诊断方法 ,并与灰色理论的 GM(1,1)预测模型有机结合 ,进行故障预报。MW blast furnace blowing engine is very complex and enormous. Based on the existing monitoring and control system, an online condition monitoring and fault diagnosis system for the group of 48MW blast furnace blowing engines is developed. It has the ability of remote monitoring and diagnosis. The virtual instrument technique on the virtual instrument development platform of LabWindows/CVI is adopted. The information integration of the complex heterostructure systems, μXL Distributed Control System, Bently Vibration Monitoring System and Windows NT computer networks system, is achieved. A multi parameter comprehensive intelligent fault diagnostic method, which adopts hierarchical and assorted diagnostic strategy and integrates rule based model, cases based model, fuzzy logic and neural networks, is proposed. This multi parameter comprehensive intelligent fault diagnostic method also combines intimately and organically that the gray predictive model GM (1,1) to predict faults intelligently. Furthermore, the industrial application shows this online condition monitoring and fault diagnosis system is useful and effective.

关 键 词：高炉 鼓风机 状态监测 故障诊断 人工智能 灰色理论 虚拟仪器 信息集成 

分 类 号：TF321.8[冶金工程—冶金机械及自动化]