Data-driven methods for operational modal parameters identification: A comparison and application

Guan, Wei; Dong, Longlei; Zhou, Jian; Han, Yi

doi:10.1016/j.measurement.2018.09.052

Cited by 22 publications

(9 citation statements)

References 21 publications

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“…From table 4, the modal frequencies and modal damping ratios are identifiable. The identification results in the determined case Deter-(4C) SOBI [43] SSI [44] Deter-(4C) SOBI [43] SSI [44] Deter-(4C) agree well with the underdetermined case. However, there is a tiny error between the experimental frequencies and the identified modal frequencies, where the maximum relative error is 2.84% and the experimental results meet the requirements of practical engineering (<3%).…”

Section: Experiments On a Cantilever Beamsupporting

confidence: 64%

Sparse component analysis with optimized clustering for underdetermined blind modal identification

Guan

Dong

Yin-shan

et al. 2019

Meas. Sci. Technol.

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Modal identification performs one of the most important roles in structural dynamics analysis and structural health monitoring, especially when the input excitations are not measurable. Most of the traditional blind source separation approaches can only handle determined or overdetermined blind modal identification, where the number of observed sensors is equal to or greater than the number of active modes. When the number of observed sensors is less than the number of active modes, new methods to perform underdetermined blind modal identification should be considered. To tackle this issue, a novel operational modal identification method based on an enhanced sparse component analysis with optimized clustering is proposed. Firstly, a robust K-means clustering with differential evolution algorithm is put forward to estimate the mode shape matrix utilizing the sparse property of observed mixtures. Secondly, the modal responses are recovered by the least squares method from the incomplete knowledge of the mode shape matrix and the system outputs. Subsequently, the modal responses are transformed into a time domain through time-frequency transformation, where the modal parameters are extracted. Finally, numerical simulation and experimental verification demonstrate that in both the determined and underdetermined case, the proposed method can perform accurate and robust parameter identification of structural systems.

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Section: Experiments On a Cantilever Beamsupporting

confidence: 64%

Sparse component analysis with optimized clustering for underdetermined blind modal identification

Guan

Dong

Yin-shan

et al. 2019

Meas. Sci. Technol.

Self Cite

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“…In this section, several numerical simulation experiments were used to verify the effectiveness of the proposed method. As the natural frequency of the first few orders of a thin-walled part has a large influence on the milling stability, the same three-degree-of-freedom structure as shown in the literature [ 18 , 37 , 38 ] was used to calculate the response of a thin-walled part subjected to a milling force excitation, which is shown in Figure 3 . Then, the parameters were selected for numerical simulation and are shown in Table 1 .…”

Section: Numerical Simulation Verificationmentioning

confidence: 99%

Output-Only Time-Varying Modal Parameter Identification Method Based on the TARMAX Model for the Milling of a Thin-Walled Workpiece

Yan

et al. 2022

Micromachines

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The process parameters chosen for high-performance machining in the milling of a thin-walled workpiece are determined by a stability prediction model, which needs accurate modal parameters of the machining system. However, the in-process modal parameters are different from the offline modal parameters and are difficult to precisely obtain due to material removal. To address this problem, an accurate time-dependent autoregressive moving average with an exogenous input (TARMAX) method is proposed for the identification of the modal parameters in the milling of a thin-walled workpiece. In this process, a TARMAX model considering external force excitation is constructed to characterize the actual condition in the milling of a thin-walled workpiece. Then, recursive method and sliding window recursive method are used to identify TARMAX model parameters under time-varying cutting conditions. Subsequently, a three-degree of freedom (3-DOF) time-varying structure numerical model under theoretical milling forces and white-noise excitation is established, and the computational results show that the predicted natural frequencies using the proposed method are in close agreement with the simulated values. Finally, several experiments are designed and carried out to validate the effectiveness of the proposed method. The experimental results show that the predicted accuracy of the proposed method using actual cutting forces is 95.68%. Good agreement has been drawn in the numerical simulation and machining experiments. Our further research objectives will focus on the prediction of the damping ratios, modal stiffness, and modal mass.

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“…By applying the multiple-input multiple-output (MIMO) concept, the modal parameters of the system can be completely estimated from the response data, including vibration signals with highly-coupled and repeated modes. Guan et al [ 6 ] systematically compared four algorithms such as PCA, ICA, SOBI and LLE and analyzed their effectiveness in modal estimation. To improve the computational efficiency, Zhang et al [ 7 ] proposed a simplified data analysis process based on PCA to estimate the order of the system to be identified and the corresponding range of frequencies contained in the signal, so as to avoid performing wavelet transform (WT) in the lower-contribution frequency bandwidth.…”

Section: Introductionmentioning

confidence: 99%

Singular Spectrum Analysis for Modal Estimation from Stationary Response Only

Lin

2022

Sensors

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Conventional experimental modal analysis uses excitation and response information to estimate the frequency response function. However, many engineering structures face excitation signals that are difficult to measure, so output-only modal estimation is an important issue. In this paper, singular spectrum analysis is employed to construct a Hankel matrix of appropriate dimensions based on the measured response data, and the observability of the system state space model is used to treat the Hankel matrix as three components containing system characteristics, excitation and noise. Singular value decomposition is used to factorize the data matrix and use the characteristics of the left and right singular matrices to reduce the dimension of the data matrix to improve calculation efficiency. Furthermore, the singular spectrum is employed to estimate the minimum order to reconstruct the Hankel matrix; then, the excitation and noise components can be removed, and the system observability matrix can be obtained. By appropriately a factorizing system observability matrix, we obtain the system matrix to estimate the modal parameters. In addition, the fictitious modes produced by increasing the order of the matrix can be eliminated through the stabilization diagram.

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Data-driven methods for operational modal parameters identification: A comparison and application

Cited by 22 publications

References 21 publications

Sparse component analysis with optimized clustering for underdetermined blind modal identification

Sparse component analysis with optimized clustering for underdetermined blind modal identification

Output-Only Time-Varying Modal Parameter Identification Method Based on the TARMAX Model for the Milling of a Thin-Walled Workpiece

Singular Spectrum Analysis for Modal Estimation from Stationary Response Only

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