Modal contribution and state space order selection in operational modal analysis

Cara, Javier; Ruiz, Jesús Juan; Alarcón, Enrique; Reynders, Edwin; Roeck, Guido De

doi:10.1016/j.ymssp.2013.03.001

Cited by 53 publications

(27 citation statements)

References 22 publications

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“…These figures illustrate the automatic identification of the modal properties from the poles of the stabilization diagrams constructed with p‐LSCF technique from the dataset acquired on 24/06/2011 at 10:00 p.m. The responses due to the modes,

{\overset{falseˆ}{y}}_{m}

, estimated for the output measured by sensor A1, the total contribution of the identified modes,

δ_{\overset{falseˆ}{y}}

, and the contributions of each identified mode to the measured outputs,

δ_{{\overset{y}{true}}_{m}}

, were estimated according to the strategy described in the previous study . The uncertainty of the estimates associated with modes 5 and 9 present the largest observed values.…”

Section: Dynamic Monitoring Systemmentioning

confidence: 99%

Automated modal tracking in a football stadium suspension roof for detection of structural changes

Diord

Magalhães

Cunha

et al. 2017

Struct Control Health Monit

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Summary Considerable efforts have been made towards the development of robust and fully automated vibration‐based monitoring systems with the goal of extracting relevant information regarding the dynamic behaviour and health condition of the monitored structure. Satisfactory results have been obtained in this scientific domain by combining accurate measurement systems and fully automated output‐only modal identification techniques. In this context, the main objective of this contribution is to demonstrate in a full‐scale case study that with the application of sophisticated algorithms for the automatic tracking of modal parameters, it is possible to detect very small structural changes. Apart from describing the main features and capabilities of the autonomous monitoring system implemented to assess the structural condition of a peculiar football stadium suspension roof, this paper also outlines the main results obtained over the course of 4 years of monitoring carried out to assess the dynamic behaviour and the health condition of the roof structure. The routines developed for the online processing of the continuously collected acceleration time series include state‐of‐the‐art processing techniques, such as automated modal identification based on cluster analysis and principal components analyses combined with control charts for removal of environmental or operational effects and detection of structural changes, together with some innovative features in the context of continuous dynamic monitoring, such as the quantification of the uncertainties associated with each modal estimate and the estimation of the contribution of each mode to the measured structural response.

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{\overset{falseˆ}{y}}_{m}

, estimated for the output measured by sensor A1, the total contribution of the identified modes,

δ_{\overset{falseˆ}{y}}

, and the contributions of each identified mode to the measured outputs,

δ_{{\overset{y}{true}}_{m}}

, were estimated according to the strategy described in the previous study . The uncertainty of the estimates associated with modes 5 and 9 present the largest observed values.…”

Section: Dynamic Monitoring Systemmentioning

confidence: 99%

Automated modal tracking in a football stadium suspension roof for detection of structural changes

Diord

Magalhães

Cunha

et al. 2017

Struct Control Health Monit

View full text Add to dashboard Cite

show abstract

“…20 It displays the poles that are obtained according to different considered system orders, as a function of the estimated frequency lines. The Singular Value (SV) curves extracted from the SVD of the SSI-DATA output spectral matrix may be reported too, within the same stabilization diagram.…”

Section: Data-driven Stochastic Subspace Identificationmentioning

confidence: 99%

“…• For the correct selection of so-called system order n and for the determination of the stable poles (i.e., the poles where frequency, mode shape, and modal damping ratio estimates show to be stable and not deriving from noise or mathematical poles), a stabilization diagram may be constructed from the SSI-DATA identification outcomes. 20 It displays the poles that are obtained according to different considered system orders, as a function of the estimated frequency lines. The Singular Value (SV) curves extracted from the SVD of the SSI-DATA output spectral matrix may be reported too, within the same stabilization diagram.…”

Section: Data-driven Stochastic Subspace Identificationmentioning

confidence: 99%

Earthquake‐induced structural response output‐only identification by two different Operational Modal Analysis techniques

Pioldi

Rizzi

2017

Earthq Engng Struct Dyn

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SummaryOutput-only system identification is developed here towards assessing current modal dynamic properties of buildings under seismic excitation. Earthquake-induced structural response signals are adopted as input channels for two different Operational Modal Analysis (OMA) techniques, namely, a refined Frequency Domain Decomposition (rFDD) algorithm and an improved Data-Driven Stochastic Subspace Identification (SSI-DATA) procedure. Despite that short-duration, non-stationary, earthquake-induced structural response signals shall not fulfil traditional OMA assumptions, these implementations are specifically formulated to operate with seismic responses and simultaneous heavy damping (in terms of identification challenge), for a consistent estimation of natural frequencies, mode shapes, and modal damping ratios. A linear ten-storey frame structure under a set of ten selected earthquake base-excitation instances is numerically simulated, by comparing the results from the two identification methods. According to this study, best up-to-date, reinterpreted OMA techniques may effectively be used to characterize the current dynamic behaviour of buildings, thus allowing for potential Structural Health Monitoring approaches in the Earthquake Engineering range.

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“…An important application is system identification. () Furthermore, the Kalman filter is used to recursively compute mechanical parameters. () In addition to that, a novelty index has been defined based on estimation error of a Kalman filter in Yan et al()…”

Section: Introductionmentioning

confidence: 99%

An output-only damage identification method based on H∞ theory and state projection estimation error (SP2E)

Lenzen

Vollmering

2017

Struct Control Health Monit

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Summary In this article, a new advantageous approach for damage identification of mechanical structures based on state projection estimation error (SP2E) is proposed. The theoretical foundation relies on output‐only identification, H∞ theory, estimation error, and state projections. Finally, the proposed damage identification approach is verified by discussing results of laboratory experiments. Therein, structural changes have been applied at a mechanical structure, and the capability to localize them by SP2E has been studied subsequently. By analyzing several different structural alteration positions and extents, an experimental sensitivity study has been conducted, which confirmed the applicability of SP2E for damage localization.

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Modal contribution and state space order selection in operational modal analysis

Cited by 53 publications

References 22 publications

Automated modal tracking in a football stadium suspension roof for detection of structural changes

Automated modal tracking in a football stadium suspension roof for detection of structural changes

Earthquake‐induced structural response output‐only identification by two different Operational Modal Analysis techniques

An output-only damage identification method based on H∞ theory and state projection estimation error (SP2E)

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