Modal Identification Based on Noise Reduction from Measured Impulsive Response Functions

Measured impulsive response functions (IRFs) are inevitably contaminated with noise when a data acquisition system is used for an experimental measurement. This situation often leads to serious difficulties in identifying the modal parameters with proper accuracy. This paper presents a noise reduction method for measured IRFs based on structured low rank approximation (SLRA) so as to improve the accuracy of the modal identification. A cantilever beam experiment is used to demonstrate the performance of the proposed method. The results show that this method can remove noise from measured IRFs efficiently, and the modal parameter identifications based on the filtered IRFs are very good.