Decision rules analysis for induction motor fault diagnosis based on rough set theory

Autor:	Zhao Yue-ling, Wang Yingli
Rok vydání:	2008
Předmět:	Artificial neural network Computer science business.industry Decision rule Fault (power engineering) computer.software_genre Machine learning Set theory Pruning (decision trees) Artificial intelligence Rough set Data mining business computer Induction motor
Zdroj:	2008 27th Chinese Control Conference.
DOI:	10.1109/chicc.2008.4605450
Popis:	In order to extract simple and effective diagnostic rules from inconsistent diagnostic information, the method of fault diagnosis based on rough set theory is proposed. The efficiency of diagnostic rules is improved by pruning rules with certainty factor and introducing the coverage factor of decision rules to remove redundant information effectively. The availability of this method is proved by a fault diagnosis example of induction motor.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::6219e2a6c9d73c3aaa15f861dfc5b8f9 https://doi.org/10.1109/chicc.2008.4605450 Zobrazit plný text záznamu