Papers by Author: Cui Lin Li

Abstract: Measured vibration response signals are inevitably contaminated with noise originating from test environment as well as electronic devices. This situation often leads to serious difficulties in many applications as they require high-quality measured data. This paper presents a signal-noise separation method for measured impulsive response functions (IRFs) based on structured low rank approximation (SLRA). The proposed method was tested with a steel cantilever beam, which was subjected to impulsive excitation. Gabor transform was also employed to process measured signals. The results show that this method can give a reliable separation of signal and noise, which will enhance the accuracy of various applications that rely on measured signals.

Abstract: Measured vibration response signals 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 cancellation method for measured impulsive response functions (IRFs) based on structured low rank approximation (SLRA) so as to improve the accuracy of the modal parameters identification. Numerical study of a cantilever beam model is carried out 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.

Abstract: 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.

Abstract: This paper develops a structured low rank approximation (SLRA) method for noise elimination from a noisy impulsive response function (IRF). Cadzow’s algorithm is implemented for the SLRA on the Hankel matrix constructed by measured IRF in order to obtain a filtered IRF. Using the proposed noise elimination scheme, some important factors, such as the size of a Hankel matrix and the quantification of the noise reduction performance are evaluated. Synthesized IRFs are applied to demonstrate the performance, and illustrate the procedure as well, of the proposed scheme in the numerical study. The results indicate that this method can eliminate noise from measured IRFs efficiently.