A fast fault diagnosis method for wind turbine generator system based on rough set-decision tree

doi:10.1109/AIMSEC.2011.6010152

	A fast fault diagnosis method for wind turbine generator system based on rough set-decision tree
	Wang, Huizhong; Peng, Anqun; Wang, Xiaolan
	2011
关键词	Computer aided diagnosis Decision theory Decision trees Failure analysis Fault detection Trees (mathematics) Turbogenerators Wind turbines C4.5 decision tree algorithm Decision tree modeling Fast classification Fault diagnosis method Knowledge reduction Wind generation system Wind turbine generator systems WTGS
DOI	10.1109/AIMSEC.2011.6010152
页码	3630-3633
英文摘要	With rough set theory for knowledge reduction capability and C4.5 decision tree algorithm for fast classification of strengths, an improved rough set-decision tree model for fault diagnosis of wind generation system is built. The results show that the proposed method can not only decreases the workload of feature datum extraction, but also identifies the fault patterns rapidly and accurately, and it exhibits better engineering practicality comparing with the C4.5-based method. © 2011 IEEE.
会议录	2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce, AIMSEC 2011 - Proceedings
会议录出版者	IEEE Computer Society
语种	英语
内容类型	会议论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/116306]
专题	电气工程与信息工程学院
作者单位	School of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou, China
推荐引用方式 GB/T 7714	Wang, Huizhong,Peng, Anqun,Wang, Xiaolan. A fast fault diagnosis method for wind turbine generator system based on rough set-decision tree[C]. 见:.