Parameter study of statistics of modal parameter estimates using automated operational modal analysis

Silas S. Christensen*, Anders Brandt

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

115 Downloads (Pure)

Abstract

For any modal parameter estimation (MPE) method, there are a few control inputs that can have an impact on the modal parameter estimates. These control inputs are typically involving parameters like the maximum model order and how many time values or frequency lines that should be included in the MPE. In this paper, a comprehensive study on the influence of these parameters is conducted using the multi-reference Ibrahim Time Domain algorithm (similar to the cov-SSI method). Data from a laboratory Plexiglas plate are investigated, and an automated Operational Modal Analysis (OMA) algorithm is used to systematically select physical poles. The effect of each of the various control parameters are discussed in the paper.

Original languageEnglish
Title of host publicationDynamics of Civil Structures, Volume 2 - Proceedings of the 37th IMAC, A Conference and Exposition on Structural Dynamics, 2019
EditorsShamim Pakzad
PublisherSpringer
Publication date2020
Pages243-254
ISBN (Print)978-3-030-12114-3
ISBN (Electronic)978-3-030-12115-0
DOIs
Publication statusPublished - 2020
Event37th IMAC, A Conference and Exposition on Structural Dynamics, 2019 - Orlando, United States
Duration: 28. Jan 201931. Jan 2019

Conference

Conference37th IMAC, A Conference and Exposition on Structural Dynamics, 2019
Country/TerritoryUnited States
CityOrlando
Period28/01/201931/01/2019
SeriesConference Proceedings of the Society for Experimental Mechanics Series
ISSN2191-5644

Keywords

  • Automated OMA (AOMA)
  • Damping
  • Modal parameter estimation (MPE)
  • Operational modal analysis (OMA)
  • Structural health monitoring (SHM)

Fingerprint

Dive into the research topics of 'Parameter study of statistics of modal parameter estimates using automated operational modal analysis'. Together they form a unique fingerprint.

Cite this