Search Results - "Fassois, S." :: K.UTB vyhledávací portál

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On-board vibration-based robust and unsupervised degradation detection in railway suspensions under various travelling speeds via a Multiple Model framework

by Vlachospyros, G., Fassois, S. D., Sakellariou, J. S.
Published in Vehicle system dynamics (02.06.2024)

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Non-stationary random vibration modelling and analysis via functional series time-dependent ARMA (FS-TARMA) models – A critical survey

by Spiridonakos, M.D., Fassois, S.D.
Published in Mechanical systems and signal processing (03.08.2014)

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A functional model based statistical time series method for vibration based damage detection, localization, and magnitude estimation

by Kopsaftopoulos, F.P., Fassois, S.D.
Published in Mechanical systems and signal processing (01.08.2013)

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Vibration based health monitoring for a lightweight truss structure: Experimental assessment of several statistical time series methods

by Kopsaftopoulos, F.P., Fassois, S.D.
Published in Mechanical systems and signal processing (01.10.2010)

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Random vibration-based progressive fatigue damage monitoring in thermoplastic coupons: a preliminary investigation

by Tsivouraki, N, Tserpes, K, Fassois, S
Published in Journal of physics. Conference series (01.02.2024)

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Parametric time-domain methods for non-stationary random vibration modelling and analysis — A critical survey and comparison

by Poulimenos, A.G., Fassois, S.D.
Published in Mechanical systems and signal processing (01.05.2006)

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Track segment automated characterisation via railway-vehicle-based random vibration signals and statistical time series methods

by Iliopoulos, I. A., Sakellariou, J. S., Fassois, S. D.
Published in Vehicle system dynamics (03.10.2022)

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A global statistical model based approach for vibration response-only damage detection under various temperatures: A proof-of-concept study

by Hios, J.D., Fassois, S.D.
Published in Mechanical systems and signal processing (20.12.2014)

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Global identification of stochastic dynamical systems under different pseudo-static operating conditions: The functionally pooled ARMAX case

by Sakellariou, J.S., Fassois, S.D.
Published in Mechanical systems and signal processing (01.01.2017)

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Parametric identification of a time-varying structure based on vector vibration response measurements

by Spiridonakos, M.D., Fassois, S.D.
Published in Mechanical systems and signal processing (01.08.2009)

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Multi-site damage precise localization via the random vibration functional model based method: Formulation & concept validation

by Sakaris, C.S., Sakellariou, J.S., Fassois, S.D.
Published in Mechanical systems and signal processing (01.11.2021)

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Robust early detection of hunting on railway vehicles: single-sensor based methods and critical comparisons

by Kritikakos, K., Fassois, S.D., Sakellariou, J.S.
Published in International journal of rail transportation (Online) (03.05.2024)

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Stationary and non-stationary random vibration modelling and analysis for an operating wind turbine

by Avendaño-Valencia, L.D., Fassois, S.D.
Published in Mechanical systems and signal processing (03.08.2014)

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A time series generalized functional model based method for vibration-based damage precise localization in structures consisting of 1D, 2D, and 3D elements

by Sakaris, C.S., Sakellariou, J.S., Fassois, S.D.
Published in Mechanical systems and signal processing (01.06.2016)

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Development and experimental validation of a prototype system for Machine Learning based SHM in composite aerostructures

by Spiliotopoulos, P E, Fera, F T, Saramantas, I E, Sakellariou, J S, Fassois, S D, Ofir, Y, Kressel, I, Tur, M, Papadopoulos, P, Giannopoulos, F, Spandonidis, C
Published in Journal of physics. Conference series (01.02.2024)

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A vibration-based Machine Learning type Structural Health Monitoring methodology for populations of composite aerostructures under uncertainty

by Saramantas, I E, Spiliotopoulos, P E, Fera, F T, Bourdalos, D M, Sakellariou, J S, Fassois, S D, Ofir, Y, Kressel, I, Tur, M, Spandonidis, C
Published in Journal of physics. Conference series (01.02.2024)

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