Sparse Signal Representation of Ultrasonic Signals for Structural Health Monitoring Applications

Assessment of the integrity of structural components is of great importance for aerospace systems, land and marine transportation, civil infrastructures and other biological and mechanical applications. Guided waves (GWs) based inspections are an attractive mean for structural health monitoring. In...

Full description

Bibliographic Details
Main Author: Perelli, Alessandro <1985>
Other Authors: Masetti, Guido
Format: Doctoral Thesis
Language:en
Published: Alma Mater Studiorum - Università di Bologna 2014
Subjects:
Online Access:http://amsdottorato.unibo.it/6321/
id ndltd-unibo.it-oai-amsdottorato.cib.unibo.it-6321
record_format oai_dc
spelling ndltd-unibo.it-oai-amsdottorato.cib.unibo.it-63212014-09-02T05:02:00Z Sparse Signal Representation of Ultrasonic Signals for Structural Health Monitoring Applications Perelli, Alessandro <1985> ING-INF/01 Elettronica Assessment of the integrity of structural components is of great importance for aerospace systems, land and marine transportation, civil infrastructures and other biological and mechanical applications. Guided waves (GWs) based inspections are an attractive mean for structural health monitoring. In this thesis, the study and development of techniques for GW ultrasound signal analysis and compression in the context of non-destructive testing of structures will be presented. In guided wave inspections, it is necessary to address the problem of the dispersion compensation. A signal processing approach based on frequency warping was adopted. Such operator maps the frequencies axis through a function derived by the group velocity of the test material and it is used to remove the dependence on the travelled distance from the acquired signals. Such processing strategy was fruitfully applied for impact location and damage localization tasks in composite and aluminum panels. It has been shown that, basing on this processing tool, low power embedded system for GW structural monitoring can be implemented. Finally, a new procedure based on Compressive Sensing has been developed and applied for data reduction. Such procedure has also a beneficial effect in enhancing the accuracy of structural defects localization. This algorithm uses the convolutive model of the propagation of ultrasonic guided waves which takes advantage of a sparse signal representation in the warped frequency domain. The recovery from the compressed samples is based on an alternating minimization procedure which achieves both an accurate reconstruction of the ultrasonic signal and a precise estimation of waves time of flight. Such information is used to feed hyperbolic or elliptic localization procedures, for accurate impact or damage localization. Alma Mater Studiorum - Università di Bologna Masetti, Guido 2014-05-09 Doctoral Thesis PeerReviewed application/pdf en http://amsdottorato.unibo.it/6321/ info:eu-repo/semantics/openAccess
collection NDLTD
language en
format Doctoral Thesis
sources NDLTD
topic ING-INF/01 Elettronica
spellingShingle ING-INF/01 Elettronica
Perelli, Alessandro <1985>
Sparse Signal Representation of Ultrasonic Signals for Structural Health Monitoring Applications
description Assessment of the integrity of structural components is of great importance for aerospace systems, land and marine transportation, civil infrastructures and other biological and mechanical applications. Guided waves (GWs) based inspections are an attractive mean for structural health monitoring. In this thesis, the study and development of techniques for GW ultrasound signal analysis and compression in the context of non-destructive testing of structures will be presented. In guided wave inspections, it is necessary to address the problem of the dispersion compensation. A signal processing approach based on frequency warping was adopted. Such operator maps the frequencies axis through a function derived by the group velocity of the test material and it is used to remove the dependence on the travelled distance from the acquired signals. Such processing strategy was fruitfully applied for impact location and damage localization tasks in composite and aluminum panels. It has been shown that, basing on this processing tool, low power embedded system for GW structural monitoring can be implemented. Finally, a new procedure based on Compressive Sensing has been developed and applied for data reduction. Such procedure has also a beneficial effect in enhancing the accuracy of structural defects localization. This algorithm uses the convolutive model of the propagation of ultrasonic guided waves which takes advantage of a sparse signal representation in the warped frequency domain. The recovery from the compressed samples is based on an alternating minimization procedure which achieves both an accurate reconstruction of the ultrasonic signal and a precise estimation of waves time of flight. Such information is used to feed hyperbolic or elliptic localization procedures, for accurate impact or damage localization.
author2 Masetti, Guido
author_facet Masetti, Guido
Perelli, Alessandro <1985>
author Perelli, Alessandro <1985>
author_sort Perelli, Alessandro <1985>
title Sparse Signal Representation of Ultrasonic Signals for Structural Health Monitoring Applications
title_short Sparse Signal Representation of Ultrasonic Signals for Structural Health Monitoring Applications
title_full Sparse Signal Representation of Ultrasonic Signals for Structural Health Monitoring Applications
title_fullStr Sparse Signal Representation of Ultrasonic Signals for Structural Health Monitoring Applications
title_full_unstemmed Sparse Signal Representation of Ultrasonic Signals for Structural Health Monitoring Applications
title_sort sparse signal representation of ultrasonic signals for structural health monitoring applications
publisher Alma Mater Studiorum - Università di Bologna
publishDate 2014
url http://amsdottorato.unibo.it/6321/
work_keys_str_mv AT perellialessandro1985 sparsesignalrepresentationofultrasonicsignalsforstructuralhealthmonitoringapplications
_version_ 1716711659162042368