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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。
Based on the LabWindows / CVI virtual instrument development platform and the virtual instrument technology, according to the existing monitoring and control system of large blower unit, the on-line condition monitoring and fault diagnosis system of blower group with remote monitoring and diagnosis capability was developed. Bently vibration monitoring system, μXL distributed control system and WindowsNT computer network system, multi-complex heterogeneous system information integration. Based on the hierarchical classification strategy, a multi-parameter synthetic intelligent fault diagnosis method based on production rules, case studies, fuzzy diagnosis and neural network integration mode is proposed and combined with the GM (1,1) prediction model of gray theory. Fault prediction. The practical engineering application shows that this online condition monitoring and fault diagnosis system is effective.