Fast orthogonal search (FOS) versus fast Fourier transform (FFT) as spectral model estimations techniques applied for structural health monitoring (SHM)

El-Shafie, Ahmed; Noureldin, Aboelmagd; McGaughey, Donald R.; Hussain, Aini

doi:10.1007/s00158-011-0695-y

Cited by 19 publications

(8 citation statements)

References 15 publications

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“…FOS algorithm has a lot of practical applications including spectral model estimations, time series analysis and non-linear system control [19][20][21][22], and the proposed algorithm is based on FOS algorithm. The main goal of the proposed algorithm is to create a functional expansion of an input y(n) by choosing the best basis functions from all candidate functions P k (n), in order to minimize the mean squared error (MSE) between y(n) and the functional expansion.…”

Section: High Resolution Carrier Frequency Estimationmentioning

confidence: 99%

A Fine Frequency Estimation Algorithm Based on Fast Orthogonal Search (FOS) for Base Station Positioning Receivers

Deng

Jia

et al. 2018

Electronics

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Base station signals have been widely studied as a promising navigation and positioning signal. The time and code division-orthogonal frequency division multiplexing (TC-OFDM) signal is a novel communication and navigation fusion signal that can simultaneously implement communication and positioning services. The TC-OFDM signal multiplexes the pseudorandom noise (PRN) code, called positioning code, and the Chinese mobile multimedia broadcasting (CMMB) signal in the same frequency band. For positioning in the TC-OFDM receiver, it is necessary to acquire and track the PRN code phase and the carrier frequency. The tracking performance is directly influenced by the accuracy of the signal acquisition, especially the acquired carrier frequency accuracy. This paper focuses on the fine frequency acquisition of TC-OFDM receivers and proposes a novel fine frequency estimation algorithm, which uses a non-linear modelling method, called fast orthogonal search (FOS), to improve the frequency acquisition accuracy of TC-OFDM receivers. With this algorithm, the PRN code is first stripped off in coarse code phase. Then, the candidate functions at each of the interest frequencies are generated, which consist of pairs of sine and cosine terms. Finally, the FOS algorithm is used to detect the carrier frequency. Simulation and experimental results show that, compared with the current carrier frequency estimation algorithms, the proposed algorithm effectively improves carrier frequency estimation accuracy and then reduces the time to the first fix.

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Section: High Resolution Carrier Frequency Estimationmentioning

confidence: 99%

A Fine Frequency Estimation Algorithm Based on Fast Orthogonal Search (FOS) for Base Station Positioning Receivers

Deng

Jia

et al. 2018

Electronics

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“…Recently, several researchers introduced time-frequency representatives including the wavelet transform (WT) (Adeli & Jiang, 2006;Cruz & Salgado, 2009;Giurgiutiu & Yu, 2003;Guo & Kareem, 2016a;Khoa, 2013;Melhem & Kim, 2003;Nagarajaiah & Basu, 2009;Qiao et al, 2012;Spanos et al, 2007;Spanos & Failla, 2005;Staszewski & Robertson, 2007;Tang et al, 2010;Wong & Chen, 2001), the Fourier transform (FT), the short-time Fourier transform (STFT) (El Shafie et al, 2012;Giurgiutiu & Yu, 2003;Guo & Kareem, 2016a;Melhem & Kim, 2003;Nagarajaiah & Basu, 2009), the Hilbert transform (HT) (Feldman, 2014;Kunwar et al, 2013;Loutridis, 2004;Pines & Salvino, 2006;Roy et al, 2019;Salvino et al, 2003;Si et al, 2016) and the Wigner-Ville distribution (WVD) (Berinde et al, 2006;Bradford et al, 2006;G. Chen et al, 2013;Claasen & Mecklenbräuker, 1980;Guo & Kareem, 2016b;Martin & Flandrin, 1985;Tang et al, 2010;Wu & Chiang, 2009) as non-parametric system identification strategies (Salvino et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

“…In terms of the usage of FT in system identification, El Shafie et al (El Shafie et al, 2012) found that the STFT cannot remain robust when the moving window length is too small. Using the STFT always requires a trade-off between time and frequency resolution due to Heisenberg's uncertainty principle.…”

Section: Introductionmentioning

confidence: 99%

Rapid post-earthquake damage assessment of ageing reinforced concrete bridge piers using time-frequency analysis

Alexander

Kashani

2020

Structure and Infrastructure Engineering

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The role of signal-based nonlinear system identification methods for the rapid post-earthquake damage assessment of reinforced concrete (RC) bridge piers is explored. Experimental data from the shaking table tests of six RC columns with and without corrosion damage are used as benchmark data. The specimens are excited under three different ground motions with different time-series characteristics, structural detailing, and corrosion levels. The proposed system identification methods make use of accelerations alone (but not displacements as these are costly in-situ) to estimate the instantaneous frequency. The Wigner-Ville distribution and Hilbert transform are utilised due to their high resolution in both time and frequency domains.A combination of modal filtering and thresholding, using instantaneous amplitudes, are employed to attenuate the unreliable spikes in the Hilbert transform's instantaneous frequency estimates. Their performance is benchmarked against a moving linear regression and standard white-noise tests. The comparison of the experimental results and time-frequency analysis indicates that the Wigner-Ville distribution and the Hilbert transform can produce reliable rapid damage detection when the response amplitude is large. The Wigner-Ville distribution has better robustness and higher resolution. The robustness of the more computationally efficient Hilbert transform can be significantly improved by the introduction of modal filtering and thresholding.

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Section: Introductionmentioning

confidence: 99%

“…Damage characterization from structural responses is a critical theoretical issue in the field of structural health monitoring (SHM) [1][2][3][4][5][6][7]. Various signal processing methods such as the Fourier transform, wavelet transform, time-frequency analysis and intelligent computation [4][5][6][7] have been widely used to extract damage features from structural responses. Unlike these existing methods, fractal dimension analysis, a newly emerging mathematical tool, has been adopted in recent years to extract damage features from structural vibrational responses [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Crack detection in beams in noisy conditions using scale fractal dimension analysis of mode shapes

Bai¹,

Ostachowicz²,

Cao³

et al. 2014

Smart Mater. Struct.

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Fractal dimension analysis of mode shapes has been actively studied in the area of structural damage detection. The most prominent features of fractal dimension analysis are high sensitivity to damage and instant determination of damage location. However, an intrinsic deficiency is its susceptibility to measurement noise, likely obscuring the features of damage. To address this deficiency, this study develops a novel damage detection method, scale fractal dimension (SFD) analysis of mode shapes, based on combining the complementary merits of a stationary wavelet transform (SWT) and Katz’s fractal dimension in damage characterization. With this method, the SWT is used to decompose a mode shape into a set of scale mode shapes at scale levels, with damage information and noise separated into distinct scale mode shapes because of their dissimilar scale characteristics; the Katz’s fractal dimension individually runs on every scale mode shape in the noise-adaptive condition provided by the SWT to canvass damage. Proof of concept for the SFD analysis is performed on cracked beams simulated by the spectral finite element method; the reliability of the method is assessed using Monte Carlo simulation to mimic the operational variability in realistic damage diagnosis. The proposed method is further experimentally validated on a cracked aluminum beam with mode shapes acquired by a scanning laser vibrometer. The results show that the SFD analysis of mode shapes provides a new strategy for damage identification in noisy conditions.

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Fast orthogonal search (FOS) versus fast Fourier transform (FFT) as spectral model estimations techniques applied for structural health monitoring (SHM)

Cited by 19 publications

References 15 publications

A Fine Frequency Estimation Algorithm Based on Fast Orthogonal Search (FOS) for Base Station Positioning Receivers

A Fine Frequency Estimation Algorithm Based on Fast Orthogonal Search (FOS) for Base Station Positioning Receivers

Rapid post-earthquake damage assessment of ageing reinforced concrete bridge piers using time-frequency analysis

Crack detection in beams in noisy conditions using scale fractal dimension analysis of mode shapes

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