Optimizing automated gas turbine fault detection using statistical pattern recognition

Loukis, E; Mathioudakis, K; Papailiou, K

dc.contributor.author	Loukis, E	en
dc.contributor.author	Mathioudakis, K	en
dc.contributor.author	Papailiou, K	en
dc.date.accessioned	2014-03-01T01:10:04Z
dc.date.available	2014-03-01T01:10:04Z
dc.date.issued	1994	en
dc.identifier.issn	0742-4795	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/11304
dc.subject	Fault Detection	en
dc.subject	Gas Turbine	en
dc.subject	Statistical Pattern Recognition	en
dc.subject.classification	Engineering, Mechanical	en
dc.subject.other	Automation	en
dc.subject.other	Compressors	en
dc.subject.other	Decision theory	en
dc.subject.other	Failure analysis	en
dc.subject.other	Identification (control systems)	en
dc.subject.other	Optimization	en
dc.subject.other	Pattern recognition	en
dc.subject.other	Statistical methods	en
dc.subject.other	Turbomachine blades	en
dc.subject.other	Decision making	en
dc.subject.other	Discriminants	en
dc.subject.other	Fault diagnosis	en
dc.subject.other	Gas turbines	en
dc.title	Optimizing automated gas turbine fault detection using statistical pattern recognition	en
heal.type	journalArticle	en
heal.identifier.primary	10.1115/1.2906787	en
heal.identifier.secondary	http://dx.doi.org/10.1115/1.2906787	en
heal.language	English	en
heal.publicationDate	1994	en
heal.abstract	A method enabling the automated diagnosis of gas turbine compressor blade faults, based on the principles of statistical pattern recognition, is initially presented. The decision making is based on the derivation of spectral patterns from dynamic measurement data and then the calculation of discriminants with respect to reference spectral patterns of the faults while it takes into account their statistical properties. A method of optimizing the selection of discriminants using dynamic measurement data is also presented. A few scalar discriminants are derived, in such a way that the maximum available discrimination potential is exploited. In this way the success rate of automated decision making is further improved, while the need for intuitive discriminant selection is eliminated. The effectiveness of the proposed methods is demonstrated by application to data coming from an industrial gas turbine while extension to other aspects of fault diagnosis is discussed.	en
heal.publisher	ASME-AMER SOC MECHANICAL ENG	en
heal.journalName	Journal of Engineering for Gas Turbines and Power	en
dc.identifier.doi	10.1115/1.2906787	en
dc.identifier.isi	ISI:A1994MV01500023	en
dc.identifier.volume	116	en
dc.identifier.issue	1	en
dc.identifier.spage	165	en
dc.identifier.epage	171	en