On the identification of damping from non-stationary free decay signals using modern signal processing techniques

Ricardo I Ramírez,Luis A Montejo

doi:10.1007/s40091-015-0096-3

Abstract

A numerical implementation of system identi- fication from non-linear and non-stationary signals is pre- sented. The continuous wavelet transform (CWT) along with the complex Morlet wavelet skeleton curve extraction and Hilbert Transform (HT)-based methodologies are used for identification purposes. A comparison of the advantages of each technique in the analysis of non-stationary free decay systems is presented and improvements to the cur- rent methodologies are proposed. The HT approach offered good results in the estimation of the instantaneous ampli- tude in low damping and non-noisy signals. However, it is highly sensitive to impulses and irregularities in the signal, which affects the proper detection of frequency and amplitude parameters in real-life signals. The CWT exhibited better results for the analysis of noisy signals, from the resulting wavelet map the noise content can be distinguished from the actual system response. That is, the modes show a distinctive pattern in the map allowing proper modal extraction. However, for highly damped non- stationary decaying signals, the results are affected by the decay rate, round-up errors, and edge effects.

Highlights

In vibration-based structural health evaluation, damage is usually associated with changes or irregularities in the system dynamic response parameters (e.g., Quinones et al 2015)
The continuous wavelet transform (CWT) along with the complex Morlet wavelet skeleton curve extraction and Hilbert Transform (HT)-based methodologies are used for identification purposes
The CWT exhibited better results for the analysis of noisy signals, from the resulting wavelet map the noise content can be distinguished from the actual system response

Summary

Introduction

In vibration-based structural health evaluation, damage is usually associated with changes or irregularities in the system dynamic response parameters (e.g., Quinones et al 2015). It is of particular interest to develop a methodology capable of extracting information of a system under changing oscillations and determine parameters that characterize the structural behavior. This is accomplished by the identification of natural frequencies of the system at different stages that can later be related to changes in stiffness (e.g., Curadelli et al 2008; Aguirre et al 2013; Aguirre and Montejo 2014). To evaluate the performance of the methodologies, signals with different damping

Objectives

Conclusion