QQ客服 官方微博 反馈留言

	Data mining for fault diagnosis and machine learning for rotating machinery
	Zhao, G ; Jiang, DX ; Kai, L ; Diao, JH
	2010-05-10 ; 2010-05-10
会议名称	DAMAGE ASSESSMENT OF STRUCTURES VI ; 6th International Conference on Damage Assessment of Structures (DAMAS 2005) ; Gdansk, POLAND ; Web of Science
关键词	faults diagnosis machinery learning data mining rotating machinery Materials Science, Ceramics Materials Science, Composites
中文摘要	Data mining is used not only for database analyses, but also for machine learning. The data mining technique described in this paper was used for steam turbine fault diagnostics based on continuous data measurements. The classification rules are based on standardized vibration frequency data for steam turbines and field experts' analyses of turbine vibration problems. The expert knowledge enables the steam turbine fault diagnosis system to be more powerful and accurate. The system can identify twenty types of standard steam turbine faults. The system was developed using 2000 simulated data sets. The data mining methods were then used to identify 20 explicit rules for the turbine faults. The method was also used with actual power plant data to successfully diagnose real faults. The results indicate that data mining can be effectively applied to diagnosis of rotating machinery by giving useful rules to interpret the data.
会议录出版者	TRANS TECH PUBLICATIONS LTD ; ZURICH-UETIKON ; BRANDRAIN 6, CH-8707 ZURICH-UETIKON, SWITZERLAND
语种	英语 ; 英语
内容类型	会议论文
源URL	[http://hdl.handle.net/123456789/19641]
专题	清华大学
推荐引用方式 GB/T 7714	Zhao, G,Jiang, DX,Kai, L,et al. Data mining for fault diagnosis and machine learning for rotating machinery[C]. 见:DAMAGE ASSESSMENT OF STRUCTURES VI, 6th International Conference on Damage Assessment of Structures (DAMAS 2005), Gdansk, POLAND, Web of Science.

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。