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AuthorNita, G.M.
AuthorMahgoub, M.A.
AuthorSharyatpanahi, S.G.
AuthorCretu, N.C.
AuthorEl-Fouly, T.M.
Available date2021-02-08T09:14:54Z
Publication Date2017
Publication NameMechanical Systems and Signal Processing
ResourceScopus
URIhttp://dx.doi.org/10.1016/j.ymssp.2016.07.004
URIhttp://hdl.handle.net/10576/17612
AbstractExperimental methods based on modal analysis under ambient vibrational excitation are often employed to detect structural damages of mechanical systems. Many of such frequency domain methods, such as Basic Frequency Domain (BFD), Frequency Domain Decomposition (FFD), or Enhanced Frequency Domain Decomposition (EFFD), use as first step a Fast Fourier Transform (FFT) estimate of the power spectral density (PSD) associated with the response of the system. In this study it is shown that higher order statistical estimators such as Spectral Kurtosis (SK) and Sample to Model Ratio (SMR) may be successfully employed not only to more reliably discriminate the response of the system against the ambient noise fluctuations, but also to better identify and separate contributions from closely spaced individual modes. It is shown that a SMR-based Maximum Likelihood curve fitting algorithm may improve the accuracy of the spectral shape and location of the individual modes and, when combined with the SK analysis, it provides efficient means to categorize such individual spectral components according to their temporal dynamics as coherent or incoherent system responses to unknown ambient excitations.
Sponsorhttp://www.esat.kuleuven.be/pub/SISTA/data/mechanical/flexible%20structure.dat.gz . This research was made possible by NPRP 6-150-2-0597 grant from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Languageen
PublisherAcademic Press
SubjectModal analysis
Sample to model ratio estimator
Spectral kurtosis estimator
TitleHigher order statistical frequency domain decomposition for operational modal analysis
TypeArticle
Pagination100-112
Volume Number84
dc.accessType Abstract Only


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