TY - JOUR
T1 - Higher order statistical frequency domain decomposition for operational modal analysis
AU - Nita, Gelu
AU - Mahgoub, Mohamed
AU - Sharyatpanahi, S. G.
AU - Cretu, N. C.
AU - El-Fouly, T. M.
N1 - Funding Information:
http://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.
Publisher Copyright:
© 2016 Elsevier Ltd
PY - 2017/2/1
Y1 - 2017/2/1
N2 - Experimental 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.
AB - Experimental 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.
KW - Modal analysis
KW - Sample to model ratio estimator
KW - Spectral kurtosis estimator
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U2 - 10.1016/j.ymssp.2016.07.004
DO - 10.1016/j.ymssp.2016.07.004
M3 - Article
AN - SCOPUS:84991821238
SN - 0888-3270
VL - 84
SP - 100
EP - 112
JO - Mechanical Systems and Signal Processing
JF - Mechanical Systems and Signal Processing
ER -