Nonlinear mode decomposition-based methodology for modal parameters identification of civil structures using ambient vibrations

Yanez-Borjas, Jesus J.; Amezquita-Sanchez, Juan P.; Valtierra-Rodríguez, Martín; Camarena-Martinez, David

doi:10.1088/1361-6501/ab3fe2

Cited by 9 publications

(7 citation statements)

References 67 publications

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“…where the superscript 'H' denotes the conjugate transposition operation, U ∈ C Q×N is a unitary matrix, which contains singular vectors, and it corresponds to Ψ in Equation (7). Λ ∈ R N×N is the diagonal matrix, which is composed of real singular values.…”

Section: Operational Modal Analysismentioning

confidence: 99%

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An Adaptive Operational Modal Analysis under Non-White Noise Excitation Using Hybrid Neural Networks

et al. 2022

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To adaptively identify the modal parameters for time-invariant structures excited by non-white noise, this paper proposes a new operational modal analysis (OMA) method using hybrid neural networks. In this work, taking the acceleration response directly as the input data of the networks not only simplifies the data processing, but also retains all the characteristics of the data. The data processed by the output function is the output data of the network, and its peak corresponds to the modal frequency. The proposed output function greatly reduces the computational cost. In addition, a small sample dataset ensures that the hybrid neural networks identify the modal parameters with the highest accuracy in the shortest possible time. Interestingly, the hybrid neural networks combine the advantages of the convolutional neural network (CNN) and gate recurrent unit (GRU). To illustrate the advantages of the proposed method, the cantilever beam and the rudder surface excited by white and non-white noise are taken as examples for experimental verification. The results reveal that the proposed method has a strong anti-noise ability and high recognition accuracy, and is not limited by ambient excitation type.

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Section: Operational Modal Analysismentioning

confidence: 99%

“…Recently, various methods for identifying modal parameters of time-invariant structures were proposed [7][8][9][10]. Additionally, the simplest and most typical one is the peak picking (PP) method [11].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Operational Modal Analysis under Non-White Noise Excitation Using Hybrid Neural Networks

et al. 2022

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“…en, using surrogate data sets' tests to eliminate false harmonics, all remaining harmonics are summed to obtain a complete NM. As a new decomposition method, NMD has been applied to many fields, such as city engineering modeling [17], analysis of surface air temperature [6], and photoplethysmography [18].…”

Section: Nmd Nonlinear Mode Decomposition (Nmd) Wasmentioning

confidence: 99%

Extracting Modulated Annual Cycle in Climate and Ocean Time Series Using an Enhanced Harmonic Analysis

Zhang

Pan

et al. 2021

Mathematical Problems in Engineering

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Accurate extraction of the modulated annual cycle (MAC) is important for climatic and oceanic research. A variety of methods are available to extract the annual cycle with inconsistent results. Since actual annual cycles are unknown in the observation series, the reliability and applicability of the results extracted by these methods are difficult to estimate. In this study, three widely used decomposition methods, ensemble empirical mode decomposition (EEMD), nonlinear mode decomposition (NMD), and enhanced harmonic analysis (EHA), are evaluated by idealized numerical experiments for extracting modulated annual cycles from climate series. Idealized numerical experiments are carried out and show that the recently proposed EHA had the most accuracy in extracting the MAC from the constructed data. The optimal independent point (IP) number, which makes the most accurate result for EHA, can be found in each ideal experiment. In the actual experiment, two IP selection criteria are proposed for EHA to extract MAC from observations.

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“…[r ni:1ob,k ] is the kth mode (n + 1) by 1 residue mode shape matrix. It can be scaled to the UMM mode shape using equation (12).…”

Section: Experimental Modal Analysis (Ema) With An Oblique Impactmentioning

confidence: 99%

“…In general, FRF testing experimentally determines the transfer function that shows the linear relationship between the input force and output response of the system. Then, MA further post-processes the measured transfer function using various curvefitting techniques [11][12][13] to obtain important dynamic characteristic information with relation to natural frequencies, mode shapes, and modal damping.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of experimental modal analysis with fixture-free oblique impact testing based on a vector projection method

Khoo¹,

Cheng²,

Ong³

et al. 2020

Meas. Sci. Technol.

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Experimental modal analysis (EMA) with oblique excitation (i.e. oblique impact testing) is useful in improving the long testing time problem of conventional EMA with normal excitation (i.e. tri-axial normal impact testing), in order to extract all important dynamic characteristics of a 3D complex structure. In this study, a new methodology involving vector projection method is introduced to find the driving point frequency response function (FRF) in the oblique direction, without the need of special fixture with oblique-oriented impedance head. Hence, it presents a low cost and practical solution to scale the mode shape, as compared to the traditional

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Nonlinear mode decomposition-based methodology for modal parameters identification of civil structures using ambient vibrations

Cited by 9 publications

References 67 publications

An Adaptive Operational Modal Analysis under Non-White Noise Excitation Using Hybrid Neural Networks

An Adaptive Operational Modal Analysis under Non-White Noise Excitation Using Hybrid Neural Networks

Extracting Modulated Annual Cycle in Climate and Ocean Time Series Using an Enhanced Harmonic Analysis

Development and validation of experimental modal analysis with fixture-free oblique impact testing based on a vector projection method

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