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Journal ArticleDOI

Output-only observer/Kalman filter identification (O3KID)

TLDR
In this article, an output-only observer/Kalman filter identification (O3KID) method is proposed for structural health monitoring based on modal parameters, in particular for those civil infrastructures whose excitation is random in nature and in the way that it is applied to the structure (e.g., wind and traffic).
Abstract
Summary This paper presents output-only observer/Kalman filter identification (O3KID), an effective method for the identification of the dynamic model of a structure and its underlying modal parameters using only output time histories measured on the field. The method is suitable for structural health monitoring based on modal parameters, in particular, for those civil infrastructures whose excitation is random in nature and in the way that it is applied to the structure (e.g., wind and traffic) and therefore is difficult to measure. O3KID is based on a linear-time-invariant state-space model and is derived from an established and successful approach for input–output system identification, known as observer/Kalman filter identification. The paper rigorously proves the applicability of the approach to the output-only case, presents the resulting new algorithms, and demonstrates them via examples on both numerical and experimental data. Copyright © 2014 John Wiley & Sons, Ltd.

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Citations
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Journal ArticleDOI

Health Monitoring of Civil Infrastructures by Subspace System Identification Method: An Overview

TL;DR: This paper aims to review studies that have used the SSI algorithm for the damage identification and modal analysis of structures, and considers the subspace algorithm to resolve the problem of a real-world application for SHM.
Journal ArticleDOI

On the selection of user-defined parameters in data-driven stochastic subspace identification

TL;DR: In this paper, the authors focus on the time domain output-only technique called Data-Driven Stochastic Subspace Identification (DD-SSI), in order to identify modal models (frequencies, damping ratios and mode shapes).
Journal ArticleDOI

Bayesian dynamic linear models for structural health monitoring

TL;DR: In this paper, a Bayesian dynamic linear model framework for modeling the time-dependent responses of structures and external effects by breaking it into components is presented. But this model is not suitable for real-time monitoring.
Journal ArticleDOI

Empirical Validation of Bayesian Dynamic Linear Models in the Context of Structural Health Monitoring

TL;DR: This paper performs an empirical validation of BDLMs in the context of applied statistics and machine learning of Bayesian dynamic linear models.
Journal ArticleDOI

Synthesizing spatiotemporally sparse smartphone sensor data for bridge modal identification

TL;DR: Results show that the spatiotemporally sparse mobile WSN data can be used to infer modal parameters despite non-overlapping sensor operation schedule, and is tested on a pedestrian bridge and compared with a conventional reference monitoring system.
References
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Book

Adaptive filtering prediction and control

TL;DR: This unified survey focuses on linear discrete-time systems and explores the natural extensions to nonlinear systems and summarizes the theoretical and practical aspects of a large class of adaptive algorithms.
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.
Journal ArticleDOI

An eigensystem realization algorithm for modal parameter identification and model reduction

TL;DR: A new approach is introduced in conjunction with the singular value decomposition technique to derive the basic formulation of minimum order realization which is an extended version of the Ho-Kalman algorithm.
Journal ArticleDOI

N4SID: subspace algorithms for the identification of combined deterministic-stochastic systems

TL;DR: Two new N4SID algorithms to identify mixed deterministic-stochastic systems are derived and these new algorithms are compared with existing subspace algorithms in theory and in practice.
Journal ArticleDOI

Modal identification of output-only systems using frequency domain decomposition

TL;DR: By introducing a decomposition of the spectral density function matrix, the response spectra can be separated into a set of single degree of freedom systems, each corresponding to an individual mode, and close modes can be identified with high accuracy even in the case of strong noise contamination of the signals.
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