摘要
对旋转机械智能故障诊断现状进行分析,展望发展趋势.当前的状态监测与故障诊断(condition monitoringand faults diagnosis,CM&FD)系统普遍存在的智能故障决策准确率偏低、可靠性差的状况显示出,相关研究仍面临着非常严峻的挑战.对数据库知识发现(knowledge discovery in database,KDD)和粗糙集理论的介绍表明,基于粗糙集理论工具的KDD为智能诊断向更科学化的方向发展指明了一条实现的新途径.但它也引发出对在线获得的故障知识应进行知识化保护问题研究的新课题.
The intelligent faults diagnosis technology of rotating machinery is reviewed. The new trend in the special investigation is prospected. In general, both the precision degree and reliability in application of condition monitoring and faults diagnosis systems are all very low on the automatic decision--making at present. It shows out that the investigations are facing with the challenge. Knowledge Discovery in Database （KDD）, the KDD using Rough Set Theory as knowledge mining tool especially, points out a new sci- entific way for the development of machine intelligent diagnosis. But it puts forward a new demand. It is that the knowledge with regard to the faults cases should be protected with data unfailingly and effectually.
出处
《兰州理工大学学报》
CAS
北大核心
2008年第5期36-40,共5页
Journal of Lanzhou University of Technology
基金
国家自然科学基金(50875118)
甘肃省攻关项目(2GS064-A52-035-02)
关键词
旋转机械
故障诊断
数据库知识发现
诊断知识资源
rotating machinery
faults diagnosis
knowledge discovery in database
faults knowledge resource
