Stochastic modal identification in the presence of harmonic excitations

Document Type

Conference Proceeding

Date of Original Version

1-1-2015

Abstract

Structures are often subjected to harmonic excitations in addition to random ambient noise. Under such a mixed loading situation, applying classical operational modal analysis (OMA) techniques is likely to encounter difficulties for correctly identifying structural modal parameters. Generally, harmonic components are potentially mistaken for being structural modes or might bias the estimation of structural modes. As various OMA time-domain methods are modeled differently with distinct solution algorithms, adopting these methods would have different numerical conditioning and stability. The objective of this study is to identify the most effective time-domain method for stochastic modal identification in the presence of harmonic excitations. In the numerical study, dynamical responses of a 5-DOF mass-damperspring system are simulated. The system is to be excited by the harmonic loading, together with random white noise loading. The projection-driven stochastic subspace identification (SSI) method is found to be superior to the covariance-driven instrumental variable (IV) and stochastic realization algorithm (S-RA) methods. From the numerical results of the scenario that the harmonic frequency is near to a modal frequency, the estimated modal parameters from the SRA method are highly perturbed and its stability diagrams are prone to produce stable spurious poles. Meanwhile, employing the stability diagrams from the IV method totally loses the ability to identify the close modal frequency. In contrast, the SSI method could accurately identify the harmonic frequency and the structural modal information. Because of its good numerical conditioning and robust numerical stability, the SSI method is a promising tool for the operational modal analysis in the presence of harmonic excitations, even without any "modification".

Publication Title, e.g., Journal

6th International Operational Modal Analysis Conference, IOMAC 2015

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