Study of Singular Spectrum Analysis as a data-driven technique for damage diagnosis. Comparison between the time or frequency domain

David Garcia, Irina Trendafilova

Research output: Contribution to conferencePaper

1 Citation (Scopus)
22 Downloads (Pure)

Abstract

Vibration-based Structural Health Monitoring methodologies have been developed in many different applications with the aim of damage diagnosis. Recently, purely data-driven methods have been gained popularity because these methods do not assume any linearity or model in their analysis. Data-driven methods use the measured vibration signals as data-input to extract features that can conclude obtain useful information for the damage diagnosis. In this work a methodology based on Singular Spectrum Analysis (SSA) technique is presented which decomposes the measured vibration responses in a certain number of principal components which reveal the rotational patterns at any frequency in the motion. One of the steps of the methodology is to create a reference state where the observations can be compared for damage assessment. The data used to create the reference state determines how the information is represented in the reference state and therefore how meaningful and informative are the feature vectors for damage assessment. This study presents of the effect of the data representation considered on the creation of the reference state when the data is introduced in the time or frequency domain. The results obtained are different depending on the signal representation and hence they should have different interpretation when the state is created based on vibratory signals represented in the time or frequency domain.
Original languageEnglish
Number of pages6
DOIs
Publication statusPublished - 2 Feb 2018
EventInternational Conference on Engineering Vibration
2017 (ICoEV 2017)
- Sofia, Bulgaria
Duration: 4 Sep 20177 Sep 2017

Conference

ConferenceInternational Conference on Engineering Vibration
2017 (ICoEV 2017)
CountryBulgaria
CitySofia
Period4/09/177/09/17

Keywords

  • singular spectrum analysis (SSA)
  • data-driven techniques

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