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
Basis-updating for data compression of displacement maps from dynamic DIC measurements
Yen-Hao Chang,Yen-Hao Chang,Weizhuo Wang,Thorsten Siebert,Jen-Yuan Chang,John E. Mottershead +5 more
TL;DR: A new algorithm is presented that addresses the need for efficiency in full-field data processing by making use of the data itself and combining the concept of sparse representation with Gram-Schmidt orthogonalisation, and the number of basis function used to represent the data can be reduced and a concise decomposition established.
Journal ArticleDOI
Quickest detection in coupled systems
TL;DR: It is proved that the minimum of N CUSUMs is asymptotically optimal as the mean time between false alarms increases without bound.
Journal ArticleDOI
Machine-learning-based methods for output-only structural modal identification
TL;DR: In this article, a self-coding deep neural network is designed to identify the structural modal parameters from the vibration data of structures, and a complex loss function is used to restrict the training process of the neural network, making the output of the third layer the modal responses we want, and the weights of the last two layers are mode shapes.
Journal ArticleDOI
An automated operational modal analysis algorithm and its application to concrete dams
TL;DR: In this article , a three-stage automated OMA algorithm based on a combination of the second-order blind identification (SOBI) and the covariance-driven stochastic subspace identification (SSI-COV) is proposed, which takes full advantage of both parametric and nonparametric algorithms while overcoming the limitations.
Journal ArticleDOI
A framework for quantifying the value of vibration-based structural health monitoring
TL;DR: In this paper , the authors present a framework for quantification of the value of vibration-based structural health monitoring (SHM) which can be flexibly applied to different use cases, from near real time diagnostics to the prognosis of slowly evolving deterioration processes over the lifetime of a structure.
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.
Related Papers (5)
Subspace Identification for Linear Systems: Theory - Implementation - Applications
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