scispace - formally typeset
Journal ArticleDOI

Reference-based stochastic subspace identification for output-only modal analysis

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.

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Citations
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Journal ArticleDOI

Maximum likelihood identification of non-stationary operational data

TL;DR: In this article, two different approaches for merging operational mode shapes from non-stationary data are proposed, based upon a single maximum likelihood estimation procedure, which are applied to nonstationary datasets obtained by scanning laser vibrometry as well as the Z24 bridge bench mark data.
Journal ArticleDOI

An SHM approach using machine learning and statistical indicators extracted from raw dynamic measurements

TL;DR: Two machine learning algorithms are compared to identify structural changes using statistics obtained from raw dynamic data using Artificial Neural Networks and Support Vector Machines to characterize acceleration measurements directly in the time domain.
Journal ArticleDOI

An efficient stochastic-based coupled model for damage identification in plate structures

TL;DR: Results of damage identification indicate that the damage indicator coupled with ALOANN show better performance in localization and quantification compared with using ANN alone even when a noise level is assigned to modal properties.
Journal ArticleDOI

SenStore: A Scalable Cyberinfrastructure Platform for Implementation of Data-to-Decision Frameworks for Infrastructure Health Management

TL;DR: A scalable and secure cyberinfrastructure platform termed SenStore is introduced for the management and automated analysis of sensing data, which includes a hybrid database architecture that maximizes query efficiency.
Journal ArticleDOI

Autonomous Decentralized System Identification by Markov Parameter Estimation Using Distributed Smart Wireless Sensor Networks

TL;DR: A system identification strategy for single-input multi-output (SIMO) subspace system identification is proposed based on Markov parameters, specifically customized for embedment within the decentralized computational framework of a wireless sensor network.
References
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Book

System Identification: Theory for the User

Lennart Ljung
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

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, +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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