Fuzzy automata for fault diagnosis: A syntactic analysis approach

Rigatos, GG; Tzafestas, SG

dc.contributor.author	Rigatos, GG	en
dc.contributor.author	Tzafestas, SG	en
dc.date.accessioned	2014-03-01T02:42:47Z
dc.date.available	2014-03-01T02:42:47Z
dc.date.issued	2004	en
dc.identifier.issn	0302-9743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31077
dc.subject	Case Study	en
dc.subject	Dc Motor	en
dc.subject	Fault Diagnosis	en
dc.subject	Fuzzy Membership Function	en
dc.subject	Monitoring System	en
dc.subject	Normal Operator	en
dc.subject	Syntactic Analysis	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.other	Automata theory	en
dc.subject.other	DC motors	en
dc.subject.other	Electrocardiography	en
dc.subject.other	Functions	en
dc.subject.other	Mathematical models	en
dc.subject.other	Pattern recognition	en
dc.subject.other	Set theory	en
dc.subject.other	Theorem proving	en
dc.subject.other	Fault diagnosis	en
dc.subject.other	Fault patterns	en
dc.subject.other	Fuzzy automata	en
dc.subject.other	Fuzzy sets	en
dc.title	Fuzzy automata for fault diagnosis: A syntactic analysis approach	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/978-3-540-24674-9_32	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-540-24674-9_32	en
heal.language	English	en
heal.publicationDate	2004	en
heal.abstract	Fuzzy automata are proposed for fault diagnosis. The output of the monitored system is partitioned into linear segments which are assigned to pattern classes (templates) with the use of fuzzy membership functions. A sequence of templates is generated and becomes input to fuzzy automata which have transitions that correspond to the templates of the properly functioning system. If the automata reach their final states, i.e. the input sequence is accepted by the automata with a membership degree that exceeds a certain threshold, then normal operation is deduced, otherwise, a failure is diagnosed. Fault diagnosis of a DC motor and detection of abnormalities in the ECG signal are used as case studies.	en
heal.publisher	SPRINGER-VERLAG BERLIN	en
heal.journalName	Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)	en
heal.bookName	LECTURE NOTES IN COMPUTER SCIENCE	en
dc.identifier.doi	10.1007/978-3-540-24674-9_32	en
dc.identifier.isi	ISI:000221610800032	en
dc.identifier.volume	3025	en
dc.identifier.spage	301	en
dc.identifier.epage	310	en