Combined Deterministic-Stochastic Frequency-Domain Subspace Identification for Experimental and Operational Modal Analysis
Bart Cauberghe, Patrick Guillaume, Pieter Verboven, Eli Parloo, Steve Vanlanduit
Abstract
Bart Cauberghe, Patrick Guillaume, Pieter Verboven, Eli Parloo, Steve Vanlanduit
Abstract
Until recently frequency-domain subspace algorithms were limited to identify deterministic models from input/output measurements. In this paper, a combined deterministic-stochastic frequency-domain subspace algorithm is presented to estimate models from input/output spectra, frequency response functions or power spectra for application as experimental and operational modal analysis. The relation with time-domain subspace identification is elaborated. It is shown by both simulations and real-life test examples that the presented method outperforms traditional frequency-domain subspace methods.
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Until recently frequency-domain subspace algorithms were limited to identify deterministic models from input/output measurements. In this paper, a combined deterministic-stochastic frequency-domain subspace algorithm is presented to estimate models from input/output spectra, frequency response functions or power spectra for application as experimental and operational modal analysis. The relation with time-domain subspace identification is elaborated. It is shown by both simulations and real-life test examples that the presented method outperforms traditional frequency-domain subspace methods.
Key concepts: Subspace topology, Frequency domain, Operational Modal Analysis, Modal, Identification (biology), Algorithm, Computer science, Domain (mathematical analysis)