Noise elimination algorithm for modal analysis

Abstract

Modal analysis is an ongoing interdisciplinary physical issue. Modal parameters estimation is applied to determine the dynamic characteristics of structures under vibration excitation. Modal analysis is more challenging for the measured vibration response signals are contaminated with noise. This study develops a mathematical algorithm of structured low rank approximation combined with the complex exponential method to estimate the modal parameters. Physical experiments using a steel cantilever beam with ten accelerometers mounted, excited by an impulse load, demonstrate that this method can significantly eliminate noise from measured signals and accurately identify the modal frequencies and damping ratios. This study provides a fundamental mechanism of noise elimination using structured low rank approximation in physical fields.

Noise Reduction for Modal Parameter Identification of the Measured FRFs Using the Modal Peak-Based Hankel-SVD Method

“…Zhao and Ye [24] argued that the singular entropy is just like an inverse transform of the singular value sequence and proposed a difference spectrum of the singular value (DSSV) selection method to select the biggest point of the difference spectrum of the singular values. Bao et al [25] introduced the model order indicator (MOI) to the singular value selection of the IRF signal for its noise reduction, which is to select the biggest point of the MOI spectrum calculated from singular values, and the formula of MOI is like an extension of DSSV. Although the above methods achieved good results in many cases, some drawbacks still exist.…”

Identification of modal parameters from noisy transient response signals

“…Hu et al and Bao et al introduced a Cadzow's algorithm to reduce unnecessary noise from noisy FRFs, but the denoising method needs to set a reasonable noise threshold on the basis of the measured signals. The effectiveness of the denoising methods in was illustrated by simulation and experimental data, but none of the results shows that these denoising methods can remove strong background noise mixed in a forced response signal.…”

“…Alamdari et al introduced a Gaussian kernel algorithm to reduce unnecessary noise from noisy FRFs, and it is designed to localize damage in the presence of heavy noise influences by using FRFs of the damaged structure only. Hu et al and Bao et al introduced a Cadzow's algorithm to reduce unnecessary noise from noisy FRFs, but the denoising method needs to set a reasonable noise threshold on the basis of the measured signals. The effectiveness of the denoising methods in was illustrated by simulation and experimental data, but none of the results shows that these denoising methods can remove strong background noise mixed in a forced response signal.…”

Proposal for a Time-Dependent Dynamic Identification Algorithm for Structural Health Monitoring

“…The proposed algorithm aims to be used as the core estimator of timedependent identification methods devoted to the health monitoring of structures and infrastructures, being suitable for a multitude of tasks ranging from the simple operational modal analysis (in pre and post-event condition) to the complex online assessment of the structural response during seismic events for rapid damage identification.[1,2,3]. Traditional dynamic identification methods used in Operational Modal Analysis (OMA) can accurately estimate parameters like modal frequencies, damping ratios and mode shapes [4,5,6], but some methods present problems related to the difficulties in identifying close-spaced modes or uncertainties when working with noise-contaminated measurements [7,8,9]. On the other hand, most methods based on output-only data work with parametric eigenvalue decompositions of a weighted data matrix, like the Singular Value Decomposition (SVD) of the Power Spectral Density matrix (PSD) -as far as the Frequency Domain Decomposition (FDD) methods are concerned [10,11,12] -or the SVD of Hankel matrixes in case of Stochastic System Identification methods (SSI) [13,14,15].…”

“…[1,2,3]. Traditional dynamic identification methods used in Operational Modal Analysis (OMA) can accurately estimate parameters like modal frequencies, damping ratios and mode shapes [4,5,6], but some methods present problems related to the difficulties in identifying close-spaced modes or uncertainties when working with noise-contaminated measurements [7,8,9]. On the other hand, most methods based on output-only data work with parametric eigenvalue decompositions of a weighted data matrix, like the Singular Value Decomposition (SVD) of the Power Spectral Density matrix (PSD) -as far as the Frequency Domain Decomposition (FDD) methods are concerned [10,11,12] -or the SVD of Hankel matrixes in case of Stochastic System Identification methods (SSI) [13,14,15].…”