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Uncertainty quantification based on a covariance-driven stochastic subspace method |
LUO Jie, KANG Jie, SUN Jiabao, ZENG Shuhong |
Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China |
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Abstract Modal parameter identification based on covariance-driven stochastic subspace identification (SSI-COV) method has the advantages of strong robustness and high precision, and is widely used in structural operational modal analysis. In order to ensure the accuracy of modal parameter identification, the newly proposed uncertainty quantification method of modal parameters based on stochastic subspace identification (SSI) can effectively estimate the variance of modal parameters, but because when calculating each intermediate variable, it is necessary to display the Jacobian matrix, resulting in high matrix operation dimension and low calculation efficiency. Therefore, this paper proposes an efficient calculation method for modal parameter uncertainty based on SSI-COV. Firstly, calculate the variance of the vibration response correlation function, and the appropriate singular value truncation order is selected by singular value decomposition (SVD), and multiple sets of Hankel matrix perturbations are assembled from each order singular vector. Secondly, according to the first-order matrix perturbation theory, the first-order perturbations of each intermediate variable of the SSI-COV algorithm are implicitly calculated, and finally, the variance is calculated by the perturbation superposition of multiple sets of modal parameters. Finally, taking the truss structure model as an example, the proposed method is used to identify the modal parameters of the structure and calculate the modal parameter variance, and the influence of the truncated order of the singular value of the Hankel matrix dimension and related functions on the identification results is analyzed, and the results show that the calculated modal parameter variance is very close to the MCS result, and the modal parameter uncertainty can be used as an effective basis for eliminating false modes.
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Received: 01 June 2023
Published: 28 April 2024
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[1]REYNDERS E, HOUBRECHTS J, DE ROECK G. Fully automated (operational) modal analysis[J]. Mechanical Systems and Signal Processing, 2012,29: 228–250.
[2]董霄峰 ,练继建 ,杨敏 ,等.谐波干扰下海上风机结构工作模态识别[J]. 振动与冲击, 2015, 34(10): 152-156.
Dong Xiaofeng, Lian Jijian, Yang Min, et al. Modal identification of offshore wind turbine structures under harmonic interference[J]. Vibration and shock, 2015,34 (10): 152-156.
[3]秦仙蓉, 余传强, 孙远韬, 等. 基于监测数据的岸桥模态参数识别[J]. 振动与冲击, 2019, 38(20): 216-221.
Qin Xianrong, Yu Chuanqiang, Sun Yuantao, et al. Modal parameter identification of quay crane based on monitoring data[J]. Vibration and shock, 2019, 38 (20): 216-221.
[4]PEETERS B, DE ROECK G. Stochastic System Identification for Operational Modal Analysis: A Review[J]. Journal of Dynamic Systems, Measurement, and Control, 2001,123(4): 659.
[5]MAGALHÃES F, CUNHA Á. Explaining operational modal analysis with data from an arch bridge[J]. Mechanical Systems and Signal Processing, 2011,25(5): 1431–1450.
[6]REYNDERS, E. System Identification Methods for (Operational) Modal Analysis: Review and Comparison[J]. Archives of Computational Methods in Engineering, 2012,19(1): 51–124.
[7]ZHIMING Z,CHAO S,BEIBEI G. Transfer-learning guided Bayesian model updating for damage identification considering modeling uncertainty[J]. Mechanical Systems and Signal Processing,2022,166: 108426
[8]SIMOEN, E., DE ROECK, LOMBAERT, G.Dealing with uncertainty in model updating for damage assessment: A review[J]. Mechanical Systems and Signal Processing,2015,7:123–149.
[9]REYNDERS E, PINTELON R, DE ROECK G. Uncertainty bounds on modal parameters obtained from stochastic subspace identification[J]. Mechanical Systems and Signal Processing, 2008,22(4): 948–969.
[10]PINTELON R, GUILLAUME P, SCHOUKENS J. Uncertainty calculation in (operational) modal analysis[J]. Mechanical Systems and Signal Processing,2007,21(6): 2359–2373.
[11]CARDEN E P, MITA A. Challenges in developing confidence intervals on modal parameters estimated for large civil infrastructure with stochastic subspace identification[J]. Structural Control and Health Monitoring,2011,18(1):53-78.
[12]REYNDERS E P B. Uncertainty quantification in data-driven stochastic subspace identification[J]. Mechanical Systems and Signal Processing, 2021,151: 107338.
[13]BRANDT A. A signal processing framework for operational modal analysis in time and frequency domain[J]. Mechanical Systems and Signal Processing, 2019,115: 380–393.
[14]REYNDERS E, MAES K, LOMBAERT G, DE ROECK G. Uncertainty quantification in operational modal analysis with stochastic subspace identification: Validation and applications[J]. Mechanical Systems and Signal Processing, 2016,66-67: 13–30.
[15]DÖHLER M, LAM X B, MEVEL L, et al. Uncertainty quantification for modal parameters from stochastic subspace identification on multi-setup measurements[J]. Mechanical Systems and Signal Processing,2013,36(2): 562–581.
[16]ARAÚJO I G, SÁNCHEZ J A G, ANDERSEN P. Modal parameter identification based on combining transmissibility functions and blind source separation techniques[J]. Mechanical Systems and Signal Processing, 2018,105: 276–293.
[17]POURGHOLI M, GILARLUE M M. VAHDAINI T, et al. Influence of Hankel matrix dimension on system identification of structures using stochastic subspace algorithms[J]. Mechanical Systems and Signal Processing, 2023, 186: 109893.
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