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
会议录名称	2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce, AIMSEC 2011 - Proceedings
页码	3630-3633
出版者	IEEE Computer Society
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.
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
收录类别	EI
语种	英语
EI入藏号	20114014387173
EI主题词	Rough set theory
来源库	Compendex
分类代码	461.1 Biomedical Engineering - 615.8 Wind Power (Before 1993, use code 611 ) - 705.2 Electric Generators - 921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory - 961 Systems Science
引用统计	无
文献类型	会议论文
条目标识符	https://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
第一作者单位	兰州理工大学
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]:IEEE Computer Society,2011:3630-3633.