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Journal ArticleDOI

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

01 Nov 1999-Mechanical Systems and Signal Processing (Academic Press)-Vol. 13, Iss: 6, pp 855-878
TL;DR: 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.
Citations
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Journal ArticleDOI
TL;DR: In this article, a review of stochastic system identification methods that have been used to estimate the modal parameters of vibrating structures in operational conditions is presented. But it is not shown that many of these methods have an output-only counterpart.
Abstract: This paper reviews stochastic system identification methods that have been used to estimate the modal parameters of vibrating structures in operational conditions. It is found that many classical input-output methods havean output-only counterpart. For instance, the Complex Mode Indication Function (CMIF) can be applied both to Frequency Response Functions and output power and cross spectra. The Polyreference Time Domain (PTD) method applied to impulse responses is similar to the Instrumental Variable (IV) method applied to output covariances. The Eigensystem Realization Algorithm (ERA) is equivalent to stochastic subspace identification.

849 citations

Journal ArticleDOI
TL;DR: In this article, the authors used the analysis of vibration measurements as a tool for health monitoring of bridges, and the problem of separating abnormal changes from normal changes in the dynamic behaviour was identified.
Abstract: When using the analysis of vibration measurements as a tool for health monitoring of bridges, the problem arises of separating abnormal changes from normal changes in the dynamic behaviour Normal changes are caused by varying environmental conditions such as humidity, wind and most important, temperature The temperature may have an impact on the boundary conditions and the material properties Abnormal changes on the other hand are caused by a loss of stiffness somewhere along the bridge It is clear that the normal changes should not raise an alarm in the monitoring system (ie a false positive), whereas the abnormal changes may be critical for the structure's safety In the frame of the European SIMCES-project, the Z24-Bridge in Switzerland was monitored during almost one year before it was artificially damaged Black-box models are determined from the healthy-bridge data These models describe the variations of eigenfrequencies as a function of temperature New data are compared with the models If an eigenfrequency exceeds certain confidence intervals of the model, there is probably another cause than the temperature that drives the eigenfrequency variations, for instance damage Copyright © 2001 John Wiley & Sons, Ltd

788 citations

Journal ArticleDOI
TL;DR: Data normalization is a procedure to normalize datasets, so that signal changes caused by operational and environmental variations of the system can be separated from structural changes of interest, such as structural deterioration or degradation.
Abstract: Stated in its most basic form, the objective of structural health monitoring is to ascertain if damage is present or not based on measured dynamic or static characteristics of a system to be monitored. In reality, structures are subject to changing environmental and operational conditions that affect measured signals, and these ambient variations of the system can often mask subtle changes in the system’s vibration signal caused by damage. Data normalization is a procedure to normalize datasets, so that signal changes caused by operational and environmental variations of the system can be separated from structural changes of interest, such as structural deterioration or degradation. This paper first reviews the effects of environmental and operational variations on real structures as reported in the literature. Then, this paper presents research progresses that have been made in the area of data normalization.

685 citations


Cites methods from "Reference-based stochastic subspace..."

  • ...(b ) Subspace-based identification method Fritzen et al. (2003) modified an existing subspace-based identification method (Peeters & De Roeck 1999) for temperature compensation....

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Journal ArticleDOI
TL;DR: In this article, two types of features are extracted from the measurements: eigenproperties of the structure using an automated stochastic subspace identification procedure and peak indicators computed on the Fourier transform of modal filters.

490 citations


Additional excerpts

  • ...For the monitoring of bridges, actual and future trends in this domain are the use of vibration signals under ambient, unknown excitation due to wind or traffic (output-only data [5]), and the use of very large arrays of sensors (towards the concept of ”smart dust” [6,7])....

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  • ...2 Stochastic Subspace identification and Operational Modal Analysis The identification method presented in this paper is the reference-based data driven stochastic subspace method [5]....

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  • ...For more details on the theoretical aspects of the method, one should refer to [5]....

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  • ...One of the fastest and most accurate methods is based on stochastic subspace identification [20,5]....

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Journal ArticleDOI
TL;DR: In this article, the authors extensively review operational modal analysis approaches and related system identification methods and compare them in an extensive Monte Carlo simulation study, and then compare the results with the results obtained in an experimental setting.
Abstract: Operational modal analysis deals with the estimation of modal parameters from vibration data obtained in operational rather than laboratory conditions. This paper extensively reviews operational modal analysis approaches and related system identification methods. First, the mathematical models employed in identification are related to the equations of motion, and their modal structure is revealed. Then, strategies that are common to the vast majority of identification algorithms are discussed before detailing some powerful algorithms. The extraction and validation of modal parameter estimates and their uncertainties from the identified system models is discussed as well. Finally, different modal analysis approaches and algorithms are compared in an extensive Monte Carlo simulation study.

481 citations

References
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Book
01 Jan 1987
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.
Abstract: Das Buch behandelt die Systemidentifizierung in dem theoretischen Bereich, der direkte Auswirkungen auf Verstaendnis und praktische Anwendung der verschiedenen Verfahren zur Identifizierung hat. Da ...

20,436 citations

Book
01 Dec 1984
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.
Abstract: 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. Shows how to build a detailed mathematical model of a test structure and analyze and modify the structure to improve its dynamics. Covers techniques for measuring the mode, shapes, and frequencies of practical structures from turbine blades to suspension bridges.

2,525 citations

Book
22 Oct 2011
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.
Abstract: Subspace Identification for Linear Systems focuses on the theory, implementation and applications of subspace identification algorithms for linear time-invariant finitedimensional dynamical systems. These algorithms allow for a fast, straightforward and accurate determination of linear multivariable models from measured inputoutput data. The theory of subspace identification algorithms is presented in detail. Several chapters are devoted to deterministic, stochastic and combined deterministicstochastic subspace identification algorithms. For each case, the geometric properties are stated in a main 'subspace' Theorem. Relations to existing algorithms and literature are explored, as are the interconnections between different subspace algorithms. The subspace identification theory is linked to the theory of frequency weighted model reduction, which leads to new interpretations and insights. The implementation of subspace identification algorithms is discussed in terms of the robust and computationally efficient RQ and singular value decompositions, which are well-established algorithms from numerical linear algebra. The algorithms are implemented in combination with a whole set of classical identification algorithms,processing and validation tools in Xmath's ISID, a commercially available graphical user interface toolbox. The basic subspace algorithms in the book are also implemented in a set of MATLAB® files accompanying the book. An application of ISID to an industrial glass tube manufacturing process is presented in detail, illustrating the power and user-friendliness of the subspace identification algorithms and of their implementation in ISID. The identified model allows for an optimal control of the process, leading to a significant enhancement of the production quality. The applicability of subspace identification algorithms in industry is further illustrated with the application of the MATLAB® files to ten practical problems. Since all necessary data and MATLAB® files are included, the reader can easily step through these applications, and thus get more insight in the algorithms. Subspace Identification for Linear Systems is an important reference for all researchers in system theory, control theory, signal processing, automization,mechatronics, chemical, electrical, mechanical and aeronautical engineering.

2,505 citations

Book
01 Jan 1994
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.
Abstract: 1. Introduction. 2. Time-Domain Models. 3. Frequency-Domain Models. 4. Frequency Response Functions. 5. System Realization. 6. Observer Identification. 7. Frequency Domain System ID. 8. Observer/Controller ID. 9. Recursive Techniques. Appendix A: Fundamental Matrix Algebra. Appendix B: Random Variables and Kalman Filter. Appendix C: Data Acquisition.

1,079 citations

01 Jan 1965
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.
Abstract: Markov parametric algorithm for effective construction of minimal realizations of linear state-variable finite-dimensional dynamical systems from input-output data

908 citations