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Palle Andersen

Bio: Palle Andersen is an academic researcher from Aalborg University. The author has contributed to research in topics: Modal & Frequency domain decomposition. The author has an hindex of 29, co-authored 109 publications receiving 6082 citations.


Papers
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Journal ArticleDOI
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.
Abstract: In this paper a new frequency domain technique is introduced for the modal identification of output-only systems, i.e. in the case where the modal parameters must be estimated without knowing the input exciting the system. By its user friendliness the technique is closely related to the classical approach where the modal parameters are estimated by simple peak picking. However, 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. By using this decomposition technique close modes can be identified with high accuracy even in the case of strong noise contamination of the signals. Also, the technique clearly indicates harmonic components in the response signals.

1,312 citations

01 Jan 2000
TL;DR: In this paper, a decomposition of the spectral density function matrix is introduced for the modal identification of output-only systems, i.e. in the case where the modality parameters must be estimated without knowing the input of the system.
Abstract: In this paper a new frequency domain technique is introduced for the modal identification of output-only systems, i.e. in the case where the modal parameters must be estimated without knowing the input exciting the system. By its user friendliness the technique is closely related to the classical approach where the modal parameters are estimated by simple peak picking. However, 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. By using this decomposition technique close modes can be identified with high accuracy even in the case of strong noise contamination of the signals. Also, the technique clearly indicates harmonic components in the response signals.

1,103 citations

01 Jan 2000
TL;DR: In this paper, a decomposition of the spectral density function matrix is introduced for modal identification from ambient responses, i.e. in the case where the modal parameters must be estimated without knowing the input of the system.
Abstract: In this paper a new frequency domain technique is introduced for the modal identification from ambient responses, i.e. in the case where the modal parameters must be estimated without knowing the input exciting the system. By its user friendliness the technique is closely related to the classical approach where the modal parameters are estimated by simple peak picking. However, by introducing a decomposition of the spectral density function matrix, the response can be separated into a set of single degree of freedom systems, each corresponding to an individual mode. By using this decomposition technique close modes can be identified with high accuracy even in the case of strong noise contamination of the signals.

939 citations

01 Jan 2001
TL;DR: In this paper, the spectral density matrix is decomposed into a set of single degree of freedom systems, and the individual SDOF auto spectral density functions are transformed back to time domain to identify damping and frequency.
Abstract: In this paper it is explained how the damping can be estimated using the Frequency Domain Decomposition technique for output-only modal identification, i.e. in the case where the modal parameters is to be estimated without knowing the forces exciting the system. Also it is explained how the natural frequencies can be accurately estimated without being limited by the frequency resolution of the discrete Fourier transform. It is explained how the spectral density matrix is decomposed into a set of single degree of freedom systems, and how the individual SDOF auto spectral density functions are transformed back to time domain to identify damping and frequency. The technique is illustrated on a simple simulation case with 2 closely spaced modes. On this example it is illustrated how the identification is influenced by very closely spacing, by non-orthogonal modes, and by correlated input. The technique is further illustrated on the output-only identification of the Great Belt Bridge. On this example it is shown how the damping is identified on a weakly exited mode and a closely spaced mode.

343 citations

01 Jan 2001
TL;DR: In this article, the spectral density matrix is decomposed into a set of single degree of freedom systems, and the individual SDOF auto spectral density functions are transformed back to time domain to identify damping and frequency.
Abstract: In this paper it is explained how the damping can be estimated using the Frequency Domain Decomposition technique for output-only modal identification, i.e. in the case where the modal parameters is to be estimated without knowing the forces exciting the system. Also it is explained how the natural frequencies can be accurately estimated without being limited by the frequency resolution of the discrete Fourier transform. It is explained how the spectral density matrix is decomposed into a set of single degree of freedom systems, and how the individual SDOF auto spectral density functions are transformed back to time domain to identify damping and frequency. The technique is illustrated on a simple simulation case with 2 closely spaced modes. On this example it is illustrated how the identification is influenced by very closely spacing, by non-orthogonal modes, and by correlated input. The technique is further illustrated on the output-only identification of the Great Belt Bridge. On this example it is shown bow the damping is identified on a weakly exited mode and a closely spaced mode.

321 citations


Cited by
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ReportDOI
01 May 1996
TL;DR: A review of the technical literature concerning the detection, location, and characterization of structural damage via techniques that examine changes in measured structural vibration response is presented in this article, where the authors categorize the methods according to required measured data and analysis technique.
Abstract: This report contains a review of the technical literature concerning the detection, location, and characterization of structural damage via techniques that examine changes in measured structural vibration response. The report first categorizes the methods according to required measured data and analysis technique. The analysis categories include changes in modal frequencies, changes in measured mode shapes (and their derivatives), and changes in measured flexibility coefficients. Methods that use property (stiffness, mass, damping) matrix updating, detection of nonlinear response, and damage detection via neural networks are also summarized. The applications of the various methods to different types of engineering problems are categorized by type of structure and are summarized. The types of structures include beams, trusses, plates, shells, bridges, offshore platforms, other large civil structures, aerospace structures, and composite structures. The report describes the development of the damage-identification methods and applications and summarizes the current state-of-the-art of the technology. The critical issues for future research in the area of damage identification are also discussed.

2,916 citations

Journal ArticleDOI
TL;DR: In this paper, the authors provide an overview of methods to detect, locate, and characterize damage in structural and mechanical systems by examining changes in measured vibration response, including frequency, mode shape, and modal damping.
Abstract: This paper provides an overview of methods to detect, locate, and characterize damage in structural and mechanical systems by examining changes in measured vibration response. Research in vibration-based damage identification has been rapidly expanding over the last few years. The basic idea behind this technology is that modal parameters (notably frequencies, mode shapes, and modal damping) are functions of the physical properties of the structure (mass, damping, and stiffness). Therefore, changes in the physical properties will cause detectable changes in the modal properties. The motivation for the development of this technology is presented. The methods are categorized according to various criteria such as the level of damage detection provided, model-based versus non-model-based methods, and linear versus nonlinear methods. The methods are also described in general terms including difficulties associated with their implementation and their fidelity. Past, current, and future-planned applications of this technology to actual engineering systems are summarized. The paper concludes with a discussion of critical issues for future research in the area of vibration-based damage identification.

2,715 citations

Journal ArticleDOI
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.
Abstract: In this paper a new frequency domain technique is introduced for the modal identification of output-only systems, i.e. in the case where the modal parameters must be estimated without knowing the input exciting the system. By its user friendliness the technique is closely related to the classical approach where the modal parameters are estimated by simple peak picking. However, 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. By using this decomposition technique close modes can be identified with high accuracy even in the case of strong noise contamination of the signals. Also, the technique clearly indicates harmonic components in the response signals.

1,312 citations

Journal ArticleDOI
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.

1,236 citations

01 Jan 2000
TL;DR: In this paper, a decomposition of the spectral density function matrix is introduced for the modal identification of output-only systems, i.e. in the case where the modality parameters must be estimated without knowing the input of the system.
Abstract: In this paper a new frequency domain technique is introduced for the modal identification of output-only systems, i.e. in the case where the modal parameters must be estimated without knowing the input exciting the system. By its user friendliness the technique is closely related to the classical approach where the modal parameters are estimated by simple peak picking. However, 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. By using this decomposition technique close modes can be identified with high accuracy even in the case of strong noise contamination of the signals. Also, the technique clearly indicates harmonic components in the response signals.

1,103 citations