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Proceedings Paper

Compressive power spectrum sensing for vibration-based output-only system identification of structural systems in the presence of noise
Author(s): Bamrung Tau Siesakul; Kyriaki Gkoktsi; Agathoklis Giaralis
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Paper Abstract

Motivated by the need to reduce monetary and energy consumption costs of wireless sensor networks in undertaking output-only/operational modal analysis of engineering structures, this paper considers a multi-coset analog-toinformation converter for structural system identification from acceleration response signals of white noise excited linear damped structures sampled at sub-Nyquist rates. The underlying natural frequencies, peak gains in the frequency domain, and critical damping ratios of the vibrating structures are estimated directly from the sub-Nyquist measurements and, therefore, the computationally demanding signal reconstruction step is by-passed. This is accomplished by first employing a power spectrum blind sampling (PSBS) technique for multi-band wide sense stationary stochastic processes in conjunction with deterministic non-uniform multi-coset sampling patterns derived from solving a weighted least square optimization problem. Next, modal properties are derived by the standard frequency domain peak picking algorithm. Special attention is focused on assessing the potential of the adopted PSBS technique, which poses no sparsity requirements to the sensed signals, to derive accurate estimates of modal structural system properties from noisy sub- Nyquist measurements. To this aim, sub-Nyquist sampled acceleration response signals corrupted by various levels of additive white noise pertaining to a benchmark space truss structure with closely spaced natural frequencies are obtained within an efficient Monte Carlo simulation-based framework. Accurate estimates of natural frequencies and reasonable estimates of local peak spectral ordinates and critical damping ratios are derived from measurements sampled at about 70% below the Nyquist rate and for SNR as low as 0db demonstrating that the adopted approach enjoys noise immunity.

Paper Details

Date Published: 19 May 2015
PDF: 13 pages
Proc. SPIE 9484, Compressive Sensing IV, 94840K (19 May 2015); doi: 10.1117/12.2177162
Show Author Affiliations
Bamrung Tau Siesakul, City Univ. London (United Kingdom)
Kyriaki Gkoktsi, City Univ. London (United Kingdom)
Agathoklis Giaralis, City Univ. London (United Kingdom)


Published in SPIE Proceedings Vol. 9484:
Compressive Sensing IV
Fauzia Ahmad, Editor(s)

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