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Journal articles on the topic 'Modal parameter identification'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Ren, Wei-Xin, and Wei-Hua Hu. "Cable Modal Parameter Identification. II: Modal Tests." Journal of Engineering Mechanics 135, no. 1 (2009): 51–61. http://dx.doi.org/10.1061/(asce)0733-9399(2009)135:1(51).

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2

He, Can, Jian Chun Xing, and Xun Zhang. "A New Method for Modal Parameter Identification Based on Natural Excitation Technique and ARMA Model in Ambient Excitation." Advanced Materials Research 1065-1069 (December 2014): 1016–19. http://dx.doi.org/10.4028/www.scientific.net/amr.1065-1069.1016.

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Due to the interference of strong vibration signal, a big error exists in modal parameter identification based on traditional ARMA model. In order to solve this problem, a new method for modal parameter identification based on natural excitation technique (NExT) and ARMA model is presented. Firstly, NExT is used to obtain cross-correlation function of different response sequences. Then, ARMA model of cross-correlation function is established, and the parameters of ARMA model are estimated by least squares method. Finally, the modal parameter identification is achieved using the relationship be

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3

Mickleborough, N. C., and Y. L. Pi. "Modal parameter identification usingZ-transforms." International Journal for Numerical Methods in Engineering 28, no. 10 (1989): 2307–21. http://dx.doi.org/10.1002/nme.1620281008.

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4

Pei, Qiang, and Long Li. "Structural Modal Parameter Identification Based on ARMA Model." Applied Mechanics and Materials 477-478 (December 2013): 736–39. http://dx.doi.org/10.4028/www.scientific.net/amm.477-478.736.

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Structural parameter identification is a significant technology to show certain characteristics and material damage of earthquake resistant structures or building materials in structural engineering and seismic engineering field. ARMA time-series method is belonged to time-domain method. ARMA time-series method for estimating the modal parameters of structure is presented in this paper. In order to verify the accuracy of the method, analytical simulation studies are performed for a frame structure model on the basis of simulated data under white-noise excitation conditions. And the results are

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5

Zhou, Yulin, Xulei Jiang, Mingjin Zhang, Jinxiang Zhang, Hao Sun, and Xin Li. "Modal parameters identification of bridge by improved stochastic subspace identification method with Grubbs criterion." Measurement and Control 54, no. 3-4 (2021): 457–64. http://dx.doi.org/10.1177/0020294021993831.

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In the wind tunnel test of a long-span bridge model, to ensure that the dynamic characteristics of the model can satisfy the test design requirements, it is particularly important to accurately identify the modal parameters of the model. First, the stochastic subspace identification algorithm was used to analyze the modal parameters of the model in the wind tunnel test; then, Grubbs criterion was introduced to effectively eliminate outliers in the damping ratio matrix. Stochastic subspace identification algorithm with Grubbs criterion improved the accuracy of the modal parameter identification

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6

Solbeck, Jason A., and Laura R. Ray. "Damage Identification Using Sensitivity-Enhancing Control and Identified Models." Journal of Vibration and Acoustics 128, no. 2 (2005): 210–20. http://dx.doi.org/10.1115/1.2159037.

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This paper investigates a coherence approach for locating structural damage using modal frequencies and transfer function parameters identified from input-output data using Observer/Kalman filter identification (OKID). Autonomous damage identification using such forward methods generally require (i) a structural model by which to relate measured and predicted modal properties induced by damage, and (ii) good sensitivity of modal parameter changes to damage states. Using the coherence approach, a damage parameter vector comprised of a finite set of modal frequencies and transfer function parame

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7

Liu, Xiaoteng, Zirui Dong, Hongxia Ji, Zhenjiang Yue, and Jie Kang. "Uncertainty-Based Fusion Method for Structural Modal Parameter Identification." Sensors 25, no. 14 (2025): 4397. https://doi.org/10.3390/s25144397.

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The structural modal parameter identification method can be classified into time-domain and frequency-domain methods. Practically, two types of methods are characterized by different advantages, and the estimated modal parameters are always subjected to statistical uncertainties due to measurement noise. In this work, an uncertainty-based fusion method for structural mode identification is proposed to merge the advantages of different methods. The extensively applied time-domain AutoRegressive (AR) and frequency-domain Left-Matrix Fraction (LMF) models are expressed in a unified parametric mod

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8

Chiang, Dar-Yun, and Si-Tsong Huang. "Modal Parameter Identification Using Simulated Evolution." AIAA Journal 35, no. 7 (1997): 1204–8. http://dx.doi.org/10.2514/2.215.

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9

Kasai, Tokio, Keiji Komatsu, and Masa Sano. "Modal Parameter Identification of Controlled Structures." Journal of Guidance, Control, and Dynamics 20, no. 1 (1997): 184–86. http://dx.doi.org/10.2514/2.4014.

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10

Chiang, Dar-Yun, and Ming-Si Cheng. "Modal Parameter Identification from Ambient Response." AIAA Journal 37, no. 4 (1999): 513–15. http://dx.doi.org/10.2514/2.745.

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11

Ren, Wei-Xin, and Wei-Hua Hu. "Cable Modal Parameter Identification. I: Theory." Journal of Engineering Mechanics 135, no. 1 (2009): 41–50. http://dx.doi.org/10.1061/(asce)0733-9399(2009)135:1(41).

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12

Chiang, Dar-Yun, and Si-Tsong Huang. "Modal parameter identification using simulated evolution." AIAA Journal 35 (January 1997): 1204–8. http://dx.doi.org/10.2514/3.13647.

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13

Chiang, Dar-Yun, and Ming-Si Cheng. "Modal parameter identification from ambient response." AIAA Journal 37 (January 1999): 513–15. http://dx.doi.org/10.2514/3.14201.

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14

Min, Z. H., and L. M. Sun. "Wavelet-based structural modal parameter identification." Structural Control and Health Monitoring 20, no. 2 (2011): 121–38. http://dx.doi.org/10.1002/stc.474.

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15

Wang, Yi, Zhenjiang Yue, and Li Liu. "Modal Identification of Aircraft Time-varying Structure Based on Fiber Grating Measurement." Journal of Physics: Conference Series 2658, no. 1 (2023): 012014. http://dx.doi.org/10.1088/1742-6596/2658/1/012014.

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Abstract To meet the increasing mission requirements, the time-varying characteristics of the aircraft structural system have been increasingly considered and utilized. Therefore, it is significant to obtain the structural modal parameters during the mission of the aircraft. However, the traditional electrical sensors have many limitations in the online measurement of spacecraft structure, and fiber grating sensors are expected to better adapt to the requirements. The online modal parameter identification methods of time-varying structures also lack sufficient research. The traditional modal p

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16

Chen, Wei Zhen, Bing Wen Wang, and Xiao Yan Huang. "Vibration-Based Structural Damage Identification Using Grey Forecasting Model." Applied Mechanics and Materials 128-129 (October 2011): 1114–17. http://dx.doi.org/10.4028/www.scientific.net/amm.128-129.1114.

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Structural damage is too difficult to identification. In this paper, a new method using grey forecasting model to identification the structural damage with past and present data is proposed. Based on the last few years of modal parameters, structural parameters including stiffness and mass of the structural were further predicted the trend using grey forecasting model. Here proposed large structure systems with natural decay and unnatural decay. Natural decay modal parameter can be get from the grey forecasting model, compare with the modal parameter identify currently, by a set value to analy

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17

Xia, Yingzhi, Hui Li, Zhezhe Fan, and Jiyong Xiao. "Modal Parameter Identification Based on Hilbert Vibration Decomposition in Vibration Stability of Bridge Structures." Advances in Civil Engineering 2021 (February 25, 2021): 1–9. http://dx.doi.org/10.1155/2021/6688686.

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Modal parameters are important parameters for the dynamic response analysis of structures. An output-only modal parameter identification technique based on Hilbert Vibration Decomposition (HVD) is developed herein for structural modal parameter identification to (1) obtain the Free Decay Response (FDR) of a structure through free vibration or ambient vibration tests, (2) decompose the FDR into modal responses using HVD, and (3) calculate the instantaneous frequencies and instantaneous damping ratios of the modal responses to obtain the modal frequencies and modal damping ratios. A series of nu

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18

Li, Cheng, and Ling Yu. "Modal Parameter Identification Method for Structural Health Monitoring Benchmark Model." Advanced Materials Research 594-597 (November 2012): 1113–17. http://dx.doi.org/10.4028/www.scientific.net/amr.594-597.1113.

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Structural modal parameters can be identified using both output responses and input excitations or only using output responses of structures. When the input excitations are known, some frequency response functions (FRF) can be obtained and further used to accurately identify the modal frequencies and mode shapes of structures, but the identified modal damping ratio is often lack of reliability. Under ambient excitations, the peak picking method (PP) is often used to quickly identify the modal parameters of structures, but some vibration modes are missed sometimes, particularly when the dense m

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19

Gong, Lei. "Application of SVD-TLS Based Parameter Estimation Method in Aircraft Structural Modal Parameter Identification." Applied Mechanics and Materials 602-605 (August 2014): 1851–54. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.1851.

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Structural modal analysis is an important element in design and testing of an aircraft, but the determination of the model order is a difficulty, in order to solve this problem, this paper advanced a Singular Value Decomposition and Total Least Squares combined method to determine the model order, and then identify the aircraft structural modal parameters. Simulation in using of aircraft flight test telemetry vibration data prove that the model order determined by this method is reasonable, and can be used to aircraft structural modal parameters identification.

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20

Liu, Pan, Yong Xie, Shao-Jing Guo, and Guo-Ping Cai. "Identification Technology of Modal Parameter of a Strap-on Launch Vehicle." International Journal of Computational Methods 13, no. 01 (2016): 1650010. http://dx.doi.org/10.1142/s0219876216500109.

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It is generally needed to conduct ground modal tests over strap-on launch vehicle to provide modal parameters for the attitude control design and load calculating. These modal parameters can also provide basis of installing sense organs on the launch vehicle. This paper introduces a modal parameter identification method based on input-output data of the system, which can be used to check calculations of modal parameters after ground modal tests. In this paper, the double-compatible free-interface modal synthesis method is first used for the modeling of the system so as to get the input and out

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21

Fassois, S. D., K. F. Eman, and S. M. Wu. "A Linear Time-Domain Method for Structural Dynamics Identification." Journal of Vibration and Acoustics 112, no. 1 (1990): 98–106. http://dx.doi.org/10.1115/1.2930106.

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A two-stage, time-domain structural dynamics identification method, that circumvents some of the main drawbacks of current techniques, and combines good noise rejection properties with a low computational load and accurate modal parameter extraction, is proposed. In the first stage of the method a discrete model is obtained through a linear estimation scheme and a systematic and effective modeling strategy. In the second stage the modal parameters are evaluated according to a new procedure that is capable of detecting and correcting large errors that may be introduced into the modal parameter

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22

Guo, Xiangying, Changkun Li, Zhong Luo, and Dongxing Cao. "Modal Parameter Identification of Structures Using Reconstructed Displacements and Stochastic Subspace Identification." Applied Sciences 11, no. 23 (2021): 11432. http://dx.doi.org/10.3390/app112311432.

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A method of modal parameter identification of structures using reconstructed displacements was proposed in the present research. The proposed method was developed based on the stochastic subspace identification (SSI) approach and used reconstructed displacements of measured accelerations as inputs. These reconstructed displacements suppressed the high-frequency component of measured acceleration data. Therefore, in comparison to the acceleration-based modal analysis, the operational modal analysis obtained more reliable and stable identification parameters from displacements regardless of the

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23

Norris, Mark A., and Larry Silverberg. "Modal identification of gyroscopic distributed-parameter systems." AIAA Journal 28, no. 12 (1990): 2104–9. http://dx.doi.org/10.2514/3.10528.

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24

Huang, Zen‐Kwei, Sheng‐De Wang, and Te‐Son Kuo. "Multi‐modal parameter identification by automata approach." Journal of the Chinese Institute of Engineers 16, no. 5 (1993): 603–13. http://dx.doi.org/10.1080/02533839.1993.9677534.

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25

Lardiès, Joseph. "Modal parameter identification from output data only." MATEC Web of Conferences 20 (2015): 01002. http://dx.doi.org/10.1051/matecconf/20152001002.

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26

Lardies, J. "Modal parameter identification from output-only measurements." Mechanics Research Communications 24, no. 5 (1997): 521–28. http://dx.doi.org/10.1016/s0093-6413(97)00057-8.

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27

Zhang, Y., Z. Zhang, X. Xu, and H. Hua. "Modal parameter identification using response data only." Journal of Sound and Vibration 282, no. 1-2 (2005): 367–80. http://dx.doi.org/10.1016/j.jsv.2004.02.012.

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28

Sun, Xin Hui, Mu Ming Hao, and Zhen Tao Li. "Software Development in Frequency Domain Modal Parameter Identification." Applied Mechanics and Materials 239-240 (December 2012): 426–29. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.426.

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A modal parameter identification software named as N-Broband is developed in VC++ platform. The software is suitable for EMA and OMA with broband identification feature. Meanwhile it also includes narrow band and selected band modal parameter identification methods. Correlation analysis between experiment and FEA can be performed in N-Broband. The validation of N-Broband is carried out by Test.Lab modal analysis software. The result coincides with Test.Lab very well, which indicates that the developed software can be used in modal analysis of real structure.

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29

Wen, Peng, Inamullah Khan, Jie He, and Qiaofeng Chen. "Application of Improved Combined Deterministic-Stochastic Subspace Algorithm in Bridge Modal Parameter Identification." Shock and Vibration 2021 (March 10, 2021): 1–11. http://dx.doi.org/10.1155/2021/8855162.

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Modal parameter identification is considered to be one of the most important tasks in structural health monitoring because it provides a reliable reference for structural vibration control, damage severity, and operational state. Moreover, at present, the combined deterministic-stochastic subspace algorithm is cogitated as one of the key algorithms in the modal parameter identification, which is why it is widely used in the modal parameter identification of bridge structures. In this paper, a novel method is proposed, which is a time-domain identification algorithm, based on sliding window-fuz

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30

Han, Jian Ping, and Pei Juan Zheng. "Modal Parameter Identification of a Rigid Frame-Continuous Girders Bridge under Ambient Excitation by Fast Bayesian FFT Method." Advanced Materials Research 639-640 (January 2013): 985–91. http://dx.doi.org/10.4028/www.scientific.net/amr.639-640.985.

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Bayesian theory is adopted in modal parameter identification, finite element model updating and residual capacity evaluation of the structures recently. Fast Bayesian FFT modal identification approach provides a rigorous way to obtain modal parameters and well-separated modes using the fast Fourier transform under ambient excitation. Moreover, it avoids choosing the modal order or removing false modes based on the stable diagram and has its obvious advantages. In this paper, modal parameters of a rigid frame-continuous girders bridge under ambient excitation are identified by this approach. Co

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31

Bao, Xing Xian, and Cui Lin Li. "Modal Parameters Identification Based on Noise Cancellation from Measured Vibration Response Signals." Applied Mechanics and Materials 50-51 (February 2011): 828–32. http://dx.doi.org/10.4028/www.scientific.net/amm.50-51.828.

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Measured vibration response signals are inevitably contaminated with noise when a data acquisition system is used for an experimental measurement. This situation often leads to serious difficulties in identifying the modal parameters with proper accuracy. This paper presents a noise cancellation method for measured impulsive response functions (IRFs) based on structured low rank approximation (SLRA) so as to improve the accuracy of the modal parameters identification. Numerical study of a cantilever beam model is carried out to demonstrate the performance of the proposed method. The results sh

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32

Chen, Yu Jing, Min Zhang, Yuan Dong Meng, Qiang Zhao, and Jie Wen Nie. "Modal Parameter Identification of Jacket-Type Platforms Using Vibration Testing Data." Applied Mechanics and Materials 170-173 (May 2012): 2243–47. http://dx.doi.org/10.4028/www.scientific.net/amm.170-173.2243.

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How to estimate the modal parameters (including natural frequencies and mode shapes) of an offshore platform accurately is crucial for the health monitoring problems. The purpose of this paper is to identify the modal parameters of a physical jacket-type platform model from measured modal testing data using Prony’s method. In the modal testing, the identified false modes are unavoidable and bring much difficulty to determine the accurate modal parameters. To solve these problems, in this paper, the Modal Phase Collinearity (MPC) and Modal Assurance Criterion (MAC) are applied to distinguish th

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33

Xiong, Hong Xia, Ci Feng Qin, and Yang Jiang. "Research on Modal Parameter Identification of the Cable Stayed Bridges Based on the Singular Value Decomposition of the Power Spectrum." Applied Mechanics and Materials 256-259 (December 2012): 1623–26. http://dx.doi.org/10.4028/www.scientific.net/amm.256-259.1623.

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In view of the modal parameter identification when only has the output signals of the system under ambient excitations has difficulty, a new method which can identify the structural modal parameters exactly based on singular value decomposition of the power spectrum is put forward. This method is used in the modal parameter identification of a cable stayed bridge under ambient excitations, and the identification frequency is compared with the finite element computation frequency. The results indicated that this method has overcome the subjectivity in modal selection of frequency domain pick-pe

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34

Wang, Jianying, Cheng Wang, Tianshu Zhang, and Bineng Zhong. "Comparison of Different Independent Component Analysis Algorithms for Output-Only Modal Analysis." Shock and Vibration 2016 (2016): 1–25. http://dx.doi.org/10.1155/2016/6309084.

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From the principle of independent component analysis (ICA) and the uncertainty of amplitude, order, and number of source signals, this paper expounds the root reasons for modal energy uncertainty, identified order uncertainty, and modal missing in output-only modal analysis based on ICA methods. Aiming at the problem of lack of comparison and evaluation of different ICA algorithms for output-only modal analysis, this paper studies the different objective functions and optimization methods of ICA for output-only modal parameter identification. Simulation results on simply supported beam verify

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35

Lu, Zhong-Rong, Guanfu Lin, and Li Wang. "Output-only modal parameter identification of structures by vision modal analysis." Journal of Sound and Vibration 497 (April 2021): 115949. http://dx.doi.org/10.1016/j.jsv.2021.115949.

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36

Xiaoying, Guan, Xie Shengjia, Chen Guo, and Qu Meijiao. "Modal parameter identification by adaptive parameter domain with multiple genetic algorithms." Journal of Mechanical Science and Technology 34, no. 12 (2020): 4965–80. http://dx.doi.org/10.1007/s12206-020-1102-7.

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37

Chang, Jun, and Satish Nagarajaiah. "Quantum-Behaved Particle Swarm Optimization-Based Structural Modal Parameter Identification Under Ambient Excitation." International Journal of Structural Stability and Dynamics 16, no. 05 (2016): 1550008. http://dx.doi.org/10.1142/s021945541550008x.

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Under ambient excitation of white noise, structural modal parameters can be identified from cross-power spectrum calculated from structural outputs, without known input. The minimization of difference between theoretical value and test value of cross-power spectrum — the former is an equation including modal parameters to be identified, and the latter is computed based on the output-only data of structure — is adopted as an objective function for optimization. The optimal objective function value can be obtained through optimal modal parameter search. Quantum-behaved particle swarm optimizatio

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38

Hios, John D., and Spilios D. Fassois. "Stochastic Vector Identification and Uncertain Modal Parameter Estimation for a Smart Composite Beam." Advances in Science and Technology 56 (September 2008): 453–58. http://dx.doi.org/10.4028/www.scientific.net/ast.56.453.

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This study aims at identifying the modal characteristics and their uncertainties for a smart composite beam. The problem is addressed via Vector AutoRegressive with eXogenous excitation (VARX) models. The advantages of VARX modeling include simplicity of implementation, high accuracy, parsimony of representation, and capability of handling modal uncertainties. Two different approaches to assess the modal parameter uncertainties are investigated. The first is based upon linearizing the function that relates the VARX model parameters with the modal parameters, whereas the second is based upon co

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39

Jiang, Yong Hua, Hong Xu, Guang Ming Cheng, Jian Ming Wen, and Ji Jie Ma. "Study on Modal Parameters Identification Based on Stratified Sampling and Complex Morlet Wavelet Transform." Advanced Materials Research 305 (July 2011): 428–33. http://dx.doi.org/10.4028/www.scientific.net/amr.305.428.

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The natural frequency of large engineering structures are very low and closely, and it’s very difficult to excite the structures by exciter, in order to identify the modal parameters of large engineering structures, a novel modal parameters identification method based on stratified sampling and complex Morlet wavelet transform is proposed. In order to improve the precision of sampling, stratified sampling, which replaces the random sampling, is applied on random decrement method for extracting the free decrement response signal, and a method is introduced to determine the sample layer weights

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40

Wu, Haishan, and Yifeng Huang. "Modal Parameter Identification of Recursive Stochastic Subspace Method." Symmetry 15, no. 6 (2023): 1243. http://dx.doi.org/10.3390/sym15061243.

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In bridge health monitoring, in order to closely monitor the structural state changes of the bridge under heavy traffic load and other harsh environments, the monitoring system is required to give the change process of structural modal parameters. Due to the symmetric variables of bridge monitoring during operation, the evaluation needs to be completed by the recursive identification of modal parameters based on environmental excitation, especially the recursive recognition of the random subspace method with high recognition accuracy. We have studied the recursive identification methods of cov

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41

Song, Zhiqiang, and Yunhe Liu. "Dynamic Parameter Identification of Hydrodynamic Bearing-Rotor System." Shock and Vibration 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/959568.

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A new method called modal parameter genetic time domain identification was employed to study the characteristics of the bearing-rotor system. A multifrequency signal decomposition technology to identify the main components of the measured signal and reject the image mode produced by noise has been used. The first- and second-order natural frequency and damping ratios of the shaft system are identified. Furthermore, because of the deficiency of the traditional least square method, a new genetic identification method to identify the bearing dynamic characteristic parameters has been proposed. Th

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42

Pei, Qiang, and Long Li. "Structural Modal Parameter Identification Based on Natural Excitation Technique." Advanced Materials Research 859 (December 2013): 167–70. http://dx.doi.org/10.4028/www.scientific.net/amr.859.167.

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Natural excitation technique has found an increasingly wide utilization in civil engineering field. Modal parameter identification in time domain method can identify the structural parameters in the use of impulse response data as input data. The theory of NExT method is presented. By constructing a numerical simulation example under white-noise excitation, and calculating the data by NExT method, the modal parameters can be identified according to complex exponential method and time series-method in time domain. The results and analysis indicate the validity of the method and provide a refere

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43

Sani, M. S. M., M. M. Rahman, M. M. Noor, K. Kadirgama, and M. H. N. Izham. "Identification of Dynamics Modal Parameter for Car Chassis." IOP Conference Series: Materials Science and Engineering 17 (February 1, 2011): 012038. http://dx.doi.org/10.1088/1757-899x/17/1/012038.

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44

Keyhani, Ali, and Saeed Mohammadi. "Structural modal parameter identification using local mean decomposition." Measurement Science and Technology 29, no. 2 (2018): 025003. http://dx.doi.org/10.1088/1361-6501/aa97f4.

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45

Zhang, Guowen, Baoping Tang, and Zhuo Chen. "Operational modal parameter identification based on PCA-CWT." Measurement 139 (June 2019): 334–45. http://dx.doi.org/10.1016/j.measurement.2019.02.078.

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46

Yan, B. F., A. Miyamoto, and E. Brühwiler. "Wavelet transform-based modal parameter identification considering uncertainty." Journal of Sound and Vibration 291, no. 1-2 (2006): 285–301. http://dx.doi.org/10.1016/j.jsv.2005.06.005.

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47

Dorvash, S., and S. N. Pakzad. "Effects of measurement noise on modal parameter identification." Smart Materials and Structures 21, no. 6 (2012): 065008. http://dx.doi.org/10.1088/0964-1726/21/6/065008.

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48

Norris, Mark A., and Larry Silverberg. "Modal identification of self-adjoint distributed-parameter systems." Earthquake Engineering & Structural Dynamics 18, no. 5 (1989): 633–42. http://dx.doi.org/10.1002/eqe.4290180504.

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49

Hwang, Jae-Seung. "Modal Parameter-Load Identification Framework in Frequency Domain." Journal of the Wind Engineering Institute of Korea 296, no. 4 (2022): 127–35. http://dx.doi.org/10.37109/weik.2022.26.4.127.

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50

Bai, Yu, and Thomas Keller. "Modal parameter identification for a GFRP pedestrian bridge." Composite Structures 82, no. 1 (2008): 90–100. http://dx.doi.org/10.1016/j.compstruct.2006.12.008.

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