Journal ArticleDOI
Reference-based stochastic subspace identification for output-only modal analysis
Bart Peeters,Guido De Roeck +1 more
TLDR
In this paper, a novel approach of stochastic subspace identification is presented that incorporates the idea of the reference sensors already in the identification step: the row space of future outputs is projected into the rowspace of past reference outputs.About:
This article is published in Mechanical Systems and Signal Processing.The article was published on 1999-11-01. It has received 1236 citations till now. The article focuses on the topics: Operational Modal Analysis & Subspace topology.read more
Citations
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Journal ArticleDOI
An improved stochastic subspace identification for operational modal analysis
TL;DR: In this paper, an improved stochastic subspace identification algorithm is introduced to solve the low computational efficiency problem of the Data-driven Stochastic Subspace Identification (DSSA) problem.
Damage identification on the Tilff bridge by vibration monitoring using optical fiber strain sensors
TL;DR: In this paper, a complete ambient vibration survey comprising both vertical accelerations and axial strains has been carried out to assess the structural health of the 50-year old bridge of Tilff, a prestressed three-cell box-girder concrete bridge with variable height.
Journal ArticleDOI
Highway Bridge Assessment Using an Adaptive Real-Time Wireless Sensor Network
TL;DR: The platform was designed specifically to provide the capability to enable expeditious system identification, as well as load rating of highway bridges without compromising the typical data acquisition parameters employed in comparable cable-based tests.
Journal ArticleDOI
Observer Kalman Filter Identification for Output-Only Systems Using Interactive Structural Modal Identification Toolsuite
Minwoo Chang,Shamim N. Pakzad +1 more
TL;DR: In this article, an expansion of the eigenvalue realization algorithm (ERA) to identify modal parameters of output-only systems (OO) by splitting the state-space model into deterministic and stochastic subsystems (ERA-OKID-OO).
Journal ArticleDOI
Indirect bridge modal parameters identification with one stationary and one moving sensors and stochastic subspace identification
TL;DR: Both numerical and experimental results show that the proposed method can estimate the bridge modal parameters from the dynamic responses of two vehicles with acceptable accuracy.
References
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Book
System Identification: Theory for the User
TL;DR: Das Buch behandelt die Systemidentifizierung in dem theoretischen Bereich, der direkte Auswirkungen auf Verstaendnis and praktische Anwendung der verschiedenen Verfahren zur IdentifIZierung hat.
Book
Modal Testing: Theory and Practice
TL;DR: A survey of the technology of modal testing, a new method for describing the vibration properties of a structure by constructing mathematical models based on test data rather than using conventional theoretical analysis.
Book
Subspace Identification for Linear Systems: Theory - Implementation - Applications
Peter Van Overschee,Bart De Moor +1 more
TL;DR: This book focuses on the theory, implementation and applications of subspace identification algorithms for linear time-invariant finitedimensional dynamical systems, which allow for a fast, straightforward and accurate determination of linear multivariable models from measured inputoutput data.
Book
Applied system identification
TL;DR: In this paper, the authors introduce the concept of Frequency Domain System ID (FDSI) and Frequency Response Functions (FRF) for time-domain models, as well as Frequency-Domain Models with Random Variables and Kalman Filter.
Effective construction of linear state-variable models from input/output functions.
B. L. Ho,R. E. Kalman +1 more
TL;DR: Markov parametric algorithm for effective construction of minimal realizations of linear state-variable finite-dimensional dynamical systems from input-output data is presented in this article, where a Markov-parametric algorithm is used to construct the minimal realization.
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Subspace Identification for Linear Systems: Theory - Implementation - Applications
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