Adaptive estimation of FCG using nonlinear state-space models

dc.contributor.author	Μούσας, Βασίλειος Χ.	el
dc.contributor.author	Κατσικάς, Σωκράτης Κ.	el
dc.contributor.author	Λαϊνιώτης, Δημήτριος Γ.	el
dc.date.accessioned	2015-06-06T17:21:03Z
dc.date.available	2015-06-06T17:21:03Z
dc.date.issued	2015-06-06
dc.identifier.uri	http://hdl.handle.net/11400/15347
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://www.tandfonline.com/doi/abs/10.1081/SAP-200064462#.VXMn9c_tlBc	el
dc.subject	Stochastic models
dc.subject	Adaptive algorithms
dc.subject	Προσαρμοστικοί αλγορίθμοι
dc.subject	ALF
dc.subject	EKF
dc.subject	Failure prediction
dc.subject	Πρόγνωση βλαβών
dc.subject	Fatigue crack growth
dc.subject	Fatigue crack growth
dc.subject	Nonlinear FCG models
dc.subject	Μοντέλα Μη γραμμική FCG
dc.subject	Nonlinear prediction
dc.subject	Στοχαστικά μοντέλα
dc.title	Adaptive estimation of FCG using nonlinear state-space models	en
heal.type	journalArticle
heal.classification	Engineering
heal.classification	Computer science
heal.classification	Μηχανική
heal.classification	Πληροφορική
heal.classificationURI	http://skos.um.es/unescothes/C01363
heal.classificationURI	http://skos.um.es/unescothes/C00750
heal.classificationURI	N/A-Μηχανική
heal.classificationURI	N/A-Πληροφορική
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh2005004376
heal.identifier.secondary	ISSN: 07362994
heal.identifier.secondary	DOI: 10.1081/SAP
heal.language	en
heal.access	campus
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Πολιτικών Μηχανικών Τ.Ε και Μηχανικών Τοπογραφίας & Γεωπληροφορικής Τ.Ε.	el
heal.publicationDate	2005
heal.bibliographicCitation	Moussas, V., Katsikas, S. and Lainiotis, D. (2005). Adaptive estimation of FCG using nonlinear state-space models. "Stochastic Analysis and Applications", 23(4), 2005. pp. 705-722. Available from: http://www.tandfonline.com/doi/abs/10.1081/SAP-200064462#.VXMn9c_tlBc. [Accessed 15/02/2007]	en
heal.abstract	In this paper, an efficient adaptive nonlinear algorithm for estimation and identification, the so-called adaptive Lainiotis filter (ALF), is applied to the problem of fatigue crack growth (FCG) estimation, identification, and prediction of the final crack (failure). A suitable nonlinear state-space FCG model is introduced for both ALF and extended Kalman filter (EKF). Both algorithms are tested in order to compare their efficiency. Through extensive analysis and simulation, it is demonstrated that the ALF has superior performance both in FCG estimation, as well as in predicting the remaining lifetime to failure. Furthermore, it is shown that the ALF is faster and easier to implement in a parallel/distributed processing mode, and much more robust than the classic EKF.	en
heal.publisher	Taylor & Francis, Inc	en
heal.journalName	Stochastic Analysis and Applications	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true