Automated reduction of statistical errors in the estimated correlation function matrix for operational modal analysis

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Automated reduction of statistical errors in the estimated correlation function matrix for operational modal analysis. / Tarpø, Marius; Friis, Tobias; Olsen, Peter; Juul, Martin; Georgakis, Christos; Brincker, Rune.

I: Mechanical Systems and Signal Processing, Bind 132, 01.10.2019, s. 790-805.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

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Tarpø, Marius ; Friis, Tobias ; Olsen, Peter ; Juul, Martin ; Georgakis, Christos ; Brincker, Rune. / Automated reduction of statistical errors in the estimated correlation function matrix for operational modal analysis. I: Mechanical Systems and Signal Processing. 2019 ; Bind 132. s. 790-805.

Bibtex

@article{e299e919c714481e9920fdce4a178efb,
title = "Automated reduction of statistical errors in the estimated correlation function matrix for operational modal analysis",
abstract = "In operational modal analysis, the correlation function matrix is treated as multiple free decays from which system parameters are extracted. The finite time length of the measured system response, however, introduces statistical errors into the estimated correlation function matrix. These errors cause both random and bias errors that transfer to an identification process of the modal parameters. The bias error is located on the envelope of the modal correlation functions, thus violating the assumption that the correlation function matrix contains multiple free decays. Therefore, the bias error transmits to the damping estimates in operational modal analysis. In this paper, we show an automated algorithm that reduces the bias error caused by the statistical errors. This algorithm identifies erratic behaviour in the tail region of the modal correlation function and reduces this noise tail. The algorithm is tested on a simulation case and experimental data of the Heritage Court Building, Canada. Based on these studies, the algorithm reduces bias error and uncertainty on the damping estimates and increases stability in the identification process.",
keywords = "Bias reduction, Correlation function matrix, Estimation error, Operational modal analysis, Uncertainty",
author = "Marius Tarp{\o} and Tobias Friis and Peter Olsen and Martin Juul and Christos Georgakis and Rune Brincker",
year = "2019",
month = oct,
day = "1",
doi = "10.1016/j.ymssp.2019.07.024",
language = "English",
volume = "132",
pages = "790--805",
journal = "Mechanical Systems and Signal Processing",
issn = "0888-3270",
publisher = "Academic Press",

}

RIS

TY - JOUR

T1 - Automated reduction of statistical errors in the estimated correlation function matrix for operational modal analysis

AU - Tarpø, Marius

AU - Friis, Tobias

AU - Olsen, Peter

AU - Juul, Martin

AU - Georgakis, Christos

AU - Brincker, Rune

PY - 2019/10/1

Y1 - 2019/10/1

N2 - In operational modal analysis, the correlation function matrix is treated as multiple free decays from which system parameters are extracted. The finite time length of the measured system response, however, introduces statistical errors into the estimated correlation function matrix. These errors cause both random and bias errors that transfer to an identification process of the modal parameters. The bias error is located on the envelope of the modal correlation functions, thus violating the assumption that the correlation function matrix contains multiple free decays. Therefore, the bias error transmits to the damping estimates in operational modal analysis. In this paper, we show an automated algorithm that reduces the bias error caused by the statistical errors. This algorithm identifies erratic behaviour in the tail region of the modal correlation function and reduces this noise tail. The algorithm is tested on a simulation case and experimental data of the Heritage Court Building, Canada. Based on these studies, the algorithm reduces bias error and uncertainty on the damping estimates and increases stability in the identification process.

AB - In operational modal analysis, the correlation function matrix is treated as multiple free decays from which system parameters are extracted. The finite time length of the measured system response, however, introduces statistical errors into the estimated correlation function matrix. These errors cause both random and bias errors that transfer to an identification process of the modal parameters. The bias error is located on the envelope of the modal correlation functions, thus violating the assumption that the correlation function matrix contains multiple free decays. Therefore, the bias error transmits to the damping estimates in operational modal analysis. In this paper, we show an automated algorithm that reduces the bias error caused by the statistical errors. This algorithm identifies erratic behaviour in the tail region of the modal correlation function and reduces this noise tail. The algorithm is tested on a simulation case and experimental data of the Heritage Court Building, Canada. Based on these studies, the algorithm reduces bias error and uncertainty on the damping estimates and increases stability in the identification process.

KW - Bias reduction

KW - Correlation function matrix

KW - Estimation error

KW - Operational modal analysis

KW - Uncertainty

UR - http://www.scopus.com/inward/record.url?scp=85069659888&partnerID=8YFLogxK

U2 - 10.1016/j.ymssp.2019.07.024

DO - 10.1016/j.ymssp.2019.07.024

M3 - Journal article

AN - SCOPUS:85069659888

VL - 132

SP - 790

EP - 805

JO - Mechanical Systems and Signal Processing

JF - Mechanical Systems and Signal Processing

SN - 0888-3270

ER -