Automated eigensystem realisation algorithm for operational modal analysis

Zhang, Guowen; Ma, Jianwei; Chen, Zhuo; Wang, Ruirong

doi:10.1016/j.jsv.2014.03.024

Cited by 49 publications

(16 citation statements)

References 17 publications

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“…It presents a slightly improved capability to deal better with noisy signals, which allows obtaining better free-decay responses unlike random decrement technique [37]. For this reason, NExT method has been employed in this work and in several applications, such as health monitoring [38][39][40]. This method assumes that any civil structure, subjected to broadband signals as excitation source, can be represented by the following motion equation: M ..…”

Section: Methodsmentioning

confidence: 99%

A Two-Step Strategy for System Identification of Civil Structures for Structural Health Monitoring Using Wavelet Transform and Genetic Algorithms

et al. 2017

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Nowadays, the accurate identification of natural frequencies and damping ratios play an important role in smart civil engineering, since they can be used for seismic design, vibration control, and condition assessment, among others. To achieve it in practical way, it is required to instrument the structure and apply techniques which are able to deal with noise-corrupted and non-linear signals, as they are common features in real-life civil structures. In this article, a two-step strategy is proposed for performing accurate modal parameters identification in an automated manner. In the first step, it is obtained and decomposed the measured signals using the natural excitation technique and the synchrosqueezed wavelet transform, respectively. Then, the second step estimates the modal parameters by solving an optimization problem employing a genetic algorithm-based approach, where the micropopulation concept is used to improve the speed convergence as well as the accuracy of the estimated values. The accuracy and effectiveness of the proposal are tested using both the simulated response of a benchmark structure and the measurements of a real eight-story building. The obtained results show that the proposed strategy can estimate the modal parameters accurately, indicating than the proposal can be considered as an alternative to perform the abovementioned task.

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Section: Methodsmentioning

confidence: 99%

A Two-Step Strategy for System Identification of Civil Structures for Structural Health Monitoring Using Wavelet Transform and Genetic Algorithms

et al. 2017

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“…In recent years, OMA has been investigated widely. 23 A wide variety of OMA methods is now available such as the Natural Excitation Technique (NExT), 24 Random Decrement Technique (RDT), 25 Stochastic Subspace Identification (SSI), 26 Eigensystem Realization Algorithm (ERA), 27 Poly-reference Least-Squares Complex Frequency-domain (p_LSCF), 28 and Frequency Domain Decomposition technique (FDD), 29 which can give good results.…”

Section: Output-only Modal Identificationmentioning

confidence: 99%

Model updating and variability analysis of modal parameters for super high‐rise structure

Chen

2018

Concurrency and Computation

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For Structural Health Monitoring (SHM), a core prerequisite is to accurately identify the modal properties. Since the dynamic characteristics of a structure change under the effects of different environmental factors such as temperature and wind, it is necessary to determine how the environmental factors affect the modal properties. This study focuses on the Guangzhou new TV tower (GNTVT), which is a 610-m high structure. Based on the measured data from GNTVT, both the varying patterns of the operational modal parameters under the effects of different environmental factors and the Finite Element Model (FEM) updating based on Genetic Algorithm (GA) had been investigated. Then, using the updated FEM, the effect of the temperature on the modal frequency had been investigated under two situations: (1) Temperature only affects the modulus of elasticity. This situation was considered to be more important because of the significant frequency change. When temperature increases by10 • C, the maximum change of modal frequencies is 1%. (2) Temperature only affects the geometric stiffness of structure. This situation was considered to be less important because the change in the frequency is very small which may be ignored.When temperature increases by10 • C, the maximum change of modal frequencies is 0.02%. The simulated results were consistent with the measured results. KEYWORDS finite element model, modal frequency, model update, super high-rise structure, temperature INTRODUCTIONUsing the vibration-based method, the primary task of Structural Health Monitoring (SHM) is to accurately identify the structural modal properties. This is because the modal properties, eg, frequency, modal shape, and damping ratio, can accurately reflect the structural properties such as stiffness and damage. 1,2 However, changes in the environmental conditions may cause changes in these modal properties. Over the past two decades, using field measurements and dynamic tests, the effects of the environmental factors, such as temperature, wind speed, and humidity on the structural modal properties have been extensively investigated. 3-17 While the vibration-based structural damage identification approaches have gained increasing attention in research, earlier studies have shown that the environmental effect is one of the main pitfalls limiting successful applications of the vibration-based damage detection methods to real structures. [18][19][20] These studies concluded that: (1) the effect of the variations in the modal properties due to the changes in the environmental factors is highly significant. The effect may be even greater than that caused by structural damage.(2) Temperature is the environmental factor that causes severe variation in modal properties.(3) The effect of the temperature on the natural frequencies can be eliminated using intelligent artificial algorithms such as Artificial Neural Networks (ANN) and Supporting Vector Machine (SVM).The studies mentioned in the preceding literature review are mostly based on the monitoring data ...

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“…Finally, a representation pole from each cluster is introduced as physical poles . Zhang presented an automated method for ERA algorithm through hierarchal clustering and introduction of two new criterion modal similarity index and the mode energy level …”

Section: Introductionmentioning

confidence: 99%

Enhanced stabilization diagram for automated modal parameter identification based on power spectral density transmissibility functions

Khodaygan

2019

Struct Control Health Monit

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Operational modal analysis based on power spectral density transmissibility (PSDT) functions is a useful tool to identify the modal parameters with low sensitivity to excitations. For pole extraction from the PSDT function, a proper parametric identification method such as the polyreference least squares complex frequency-domain method or poly-Max method can be used. Then, the poles are selected from a stabilization diagram (SD) with overestimating the system model order. Therefore, spurious modes can be identified that must be distinguished and removed from the system poles. To reach this aim, many techniques have been proposed and applied. In this paper, a new algorithm is proposed to enhance the performance of the SD for automated modal parameter identification based on the PSDT. The algorithm is composed of two main phases. In the first phase, the spurious modes are discriminated from the system poles on the basis of the conventional and supplementary stability criteria. On spurious mode omission, two new criteria named "pole criterion" and "coherence criterion" are introduced and applied as the supplementary stability criteria to make a more clear SD. Then, the extracted poles are categorized in the distinct clusters through a new strategy for comparing modes. In the second phase, a novel multiscreening algorithm is implemented for the automated identification of the system poles. Accordingly, the searching and averaging processes are followed between clusters, and the poles are screened to automatically identify the system poles on the basis of the numbers of their repetition in the SD via k-means clustering algorithms. Also, to improve the accuracy of the identification, the Hilbert transform is used in the construction of the PSDT functions. Finally, to validate and demonstrate the efficiency of the proposed method, a computer simulation and an experiential case study are considered.

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Automated eigensystem realisation algorithm for operational modal analysis

Cited by 49 publications

References 17 publications

A Two-Step Strategy for System Identification of Civil Structures for Structural Health Monitoring Using Wavelet Transform and Genetic Algorithms

A Two-Step Strategy for System Identification of Civil Structures for Structural Health Monitoring Using Wavelet Transform and Genetic Algorithms

Model updating and variability analysis of modal parameters for super high‐rise structure

Enhanced stabilization diagram for automated modal parameter identification based on power spectral density transmissibility functions

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