大型鼓风机组在线状态监测与故障诊断  

Online Condition Monitoring and Fault Diagnosis for Large-scale Blowing Engines

作　　者：董振兴[1] 杨汝清[1] 史定国[2] 张东山[2] 

机构地区：[1]上海交通大学 [2]华东理工大学

出　　处：《流体机械》2001年第10期31-35,共5页Fluid Machinery

摘　　要：利用LabWindows/CVI虚拟仪器开发平台 ,采用虚拟仪器技术 ,根据大型鼓风机组已有的监测、控制系统的具体情况 ,开发了具有远程监测诊断能力的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WindowsNT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。实际工程应用结果表明这一在线状态监测与故障诊断系统是行之有效的。The virtual instrument technique on the Virtual Instruments development platform of LabWindows/CVI is adopted. Based on the existing monitoring and control system, the information integration of the complex heterostructure systems, μXL Distributed Control System, Bently Vibration Monitoring System and Windows NT computer networks system, is achieved and an online condition monitoring and fault diagnosis system for blowing engines is developed. A multi-parameter comprehensive intelligent fault diagnostic method, which adopts hierarchical and assorted diagnostic strategy and integrates rules-based model, cases-based model, fuzzy logic and neural networks, is proposed. Faults could been predicted by this method, which combines intimately and organically the gray predictive model GM (1,1) intelligently.

关 键 词：神经网络集成 在线状态监测 产生式规则 NT 故障预报 开发平台 虚拟仪器 综合智能 鼓风机 模糊诊断 

分 类 号：TH17[机械工程—机械制造及自动化] TP274.2[自动化与计算机技术—检测技术与自动化装置]