Extraction of modal parameters for identification of time-varying systems using data-driven stochastic subspace identification

Li, Wenchao; Vu, Viet-Hung; Liu, Zhaoheng; Thomas, Marc; Hazel, Bruce

doi:10.1177/1077546317734670

Cited by 23 publications

(7 citation statements)

References 24 publications

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“…This method is based on the discrete state-space equation of a linear system and is used to analyze the vibration response of the structure under a random stationary excitation. It includes both a data-driven approach and a covariance-driven approach (Li et al, 2018). The covariance-driven SSI method calculates the covariance matrix of the output data of the system and then decompresses the singular value of the covariance matrix to get the system matrix.…”

Section: Ssi Methods and Numerical Validationmentioning

confidence: 99%

Field measurements of typhoon effects on a transmission tower and its modal parameter identification

Ren

et al. 2020

Advances in Structural Engineering

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Many transmission towers have collapsed under typhoons in recent years, mainly due to the unclear behaviors of their structural properties, introducing many deficiencies in the design process. Therefore, implementing structural health monitoring is of great importance for investigating the structural features of large-span transmission lines. This study develops a stochastic subspace identification method to identify the modal parameters of transmission towers, and a finite element model of a transmission tower-line system is established based on a case in Guangdong Province, China. Moreover, a MATLAB program is written using the stochastic subspace identification method to perform a modal analysis on the wind-induced responses of a transmission tower, and the results are compared with those of the finite element model to verify the program’s reliability. A structural health monitoring system installed on a transmission tower recorded the wind field data around the tower and its vibration responses during Typhoon Khanun. The characteristics of the typhoon wind field and the changes in the acceleration responses under different wind speeds were calculated, and the developed stochastic subspace identification method was used to identify the frequencies and damping ratios of the tower. The results show that the identified frequencies under different wind speeds in the longitudinal and transverse directions remain essentially unchanged, indicating that the monitoring tower was safe and suffered no damage during Typhoon Khanun. The damping ratios of the monitoring tower range from 1% to 4%, where the larger values may be caused by bolt slippage.

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Section: Ssi Methods and Numerical Validationmentioning

confidence: 99%

Field measurements of typhoon effects on a transmission tower and its modal parameter identification

Ren

et al. 2020

Advances in Structural Engineering

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“…In addition, most of the work focuses on simple linear time-invariant structures. With the engineering structures becoming more complicated, time-varying [46] and nonlinearity problems, which widely exist in aerospace and mechanical structures, become increasingly prominent. For these complex structures, related studies are rather rare.…”

Section: Conclusion and Prospectsmentioning

confidence: 99%

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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“…Among state-space modeling algorithms, the data-driven stochastic subspace identification (SSI) algorithm is the most reliable OMA algorithm, [23][24][25] while the deterministic subspace identification (DSI) algorithm is the most accurate MIMO modal analysis algorithm. [26][27][28] In recent years, subspace algorithms have been widely used to solve SI problems of various civil engineering structures, such as cable-stayed bridges, 29 arch bridges, 30 suspension bridges, 31 reinforced concrete (RC) buildings, 32 super highrise towers, 33 and masonry buildings.…”

Section: Introductionmentioning

confidence: 99%

Novel tensor subspace system identification algorithm to identify time-varying modal parameters of bridge structures

Zhang

Shan

2021

Structural Health Monitoring

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Subspace-based system identification algorithms have been developed as an advanced technique for performing modal analysis. We introduce a novel tensor subspace-based algorithm to identify the time-varying modal parameters of bridge structures. A new time dimension is introduced in the traditional Hankel matrix, and a mathematical model of tensor subspace decomposition is established. Combined with the stabilization diagram, tensor parallel factor decomposition is used to estimate the frequencies, mode shapes, and modal damping ratios. The effectiveness of the proposed algorithm is validated by comparing it with the classical sliding-window–based stochastic subspace algorithm on a model cable-stayed bridge dynamic test. The proposed algorithm is further applied to process the dynamic responses of a real bridge health monitoring system to identify its time-varying modal frequencies. Our results demonstrated that the proposed algorithm significantly reduces computational efforts and extends the range of solution ideas for future out-only time-varying system identification problems.

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Extraction of modal parameters for identification of time-varying systems using data-driven stochastic subspace identification

Cited by 23 publications

References 24 publications

Field measurements of typhoon effects on a transmission tower and its modal parameter identification

Field measurements of typhoon effects on a transmission tower and its modal parameter identification

Sparse component analysis with optimized clustering for underdetermined blind modal identification

Novel tensor subspace system identification algorithm to identify time-varying modal parameters of bridge structures

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