Modal parameter identification of structure under base excitation using vibration test data

Xia, JN; Song, Hanwen

doi:10.1177/0954410016652919

Cited by 7 publications

(3 citation statements)

References 8 publications

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“…Wen [11] identified the modal parameters of a double-curved cable-stayed bridge through forced vibration experiments, and calculated the modal parameters based on frequency response curves under the different excitation conditions. Song [12] used a modal parameter identification method based on experimental vibration data to estimate the parameters of a concentrated mass mechanical system in a vibration experiment. However, the structure of the electromagnetic linear-angular vibration exciter is relatively complex and the analysis process is more cumbersome.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Coupled Vibration Characteristics of Linear-Angular and Parameter Identification

Tang,

Yang,

Chen

et al. 2024

Measurement Science Review

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A steady-state sinusoidal and distortion-free excitation source is very important for the accuracy and consistency of the calibration parameters of micro-electro-mechanical systems (MEMS) inertial sensors. To solve the problem that the current MEMS inertial measurement unit (IMU) calibration device is unable to reproduce the spatial motion of linear and angular vibration coupling, research topics on the coupling vibration characteristics and parameter identification for an electromagnetic linear-angular vibration exciter are proposed. This research paper used Ampere’s law and Lorentz force to establish the analytical expressions for the electromagnetic force and electromagnetic torque of the electromagnetic linear-angular vibration exciter. Then, the main purpose of this paper is to establish uniaxial and coupled vibration electromechanical analogy models containing mechanical parameters based on the admittance-type electromechanical analogy principle, and the parameter identification model is also obtained by combining the impedance formula with the additional mass method. Finally, the validity of the coupling vibration characteristics and the parameter identification model are verified by the frequency response simulation and the additional mass method, and the relative error of each parameter identification is within 5% in this paper.

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Section: Introductionmentioning

confidence: 99%

Analysis of Coupled Vibration Characteristics of Linear-Angular and Parameter Identification

Tang,

Yang,

Chen

et al. 2024

Measurement Science Review

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“…Transfer path analysis methods mainly include experimental approaches 6,7 and time-domain identification simulation. 8,9 Among them, experimental approaches can directly yield the transfer function through the hammer test. The vibration contribution analysis is completed by combining the results with the working load, and thus the dominant transfer path can be identified.…”

Section: Introductionmentioning

confidence: 99%

Kalman filter identification method for micro-vibration transfer paths of satellites

Shen

Yang

Sheng

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part G:

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Micro-vibrations on-board a satellite have degrading effects on the performance of certain payloads like observation cameras. The major sources of vibrations include momentum wheels, solar array drives, other rotary mechanical equipment, etc. These vibrations result in loss of the pointing precision and image quality of the payload through intricate transfer paths. To improve the accuracy of a satellite system with many vibration sources and complex transfer paths, it is necessary to determine the main transfer path of vibration. In this study, a path identification method is proposed and applied to the transfer system from the momentum wheel to the camera mount. First, the observer/Kalman filter identification (OKID) algorithm is used to acquire the state-space equation of each path subsystem. Then, the subsystem order is obtained based on the slope of the singular entropy increment. In the next phase, combined with the measured disturbance force of the momentum wheel, the displacement response of the target point is predicted. Finally, the dominant transfer path of vibration is achieved by calculating the vibration contribution of each path to the response point. The results indicate that the dominant transfer path is the axial path of the horizontal momentum wheel, which contributes to the vibration of the camera mount at most. Effective vibration reduction measures should be taken to this path to suppress the vibration signal. In comparing the identified displacement response with the finite element response of the camera mount under different noise conditions, the correlation coefficients are >0.85, which proves the accuracy and anti-noise capability of the identification method.

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“…On-orbit identification experiments of structural modal parameters had been implemented on some spacecraft, 1,2 such as the Hubble Space Telescope (HST) 3 and Engineering Test Satellite (ETS) series. [4][5][6] Currently, these identification experiments are all based on time-invariant systems.…”

Section: Introductionmentioning

confidence: 99%

Identification of the time-varying modal parameters of a spacecraft with flexible appendages using a recursive predictor-based subspace identification algorithm

Liu

2018

Proceedings of the Institution of Mechanical Engineers, Part G:

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This study focuses on the recursive identification of the time-varying modal parameters of on-orbit spacecraft caused by structural configuration changes. For this purpose, an algorithm called recursive predictor-based subspace identification is applied as an alternative method to improve the computational efficiency and noise robustness, and to implement an online identification of system parameters. In the existing time-domain identification methods, the eigensystem realization algorithm and subspace identification methods are usually applied to obtain the on-orbit spacecraft modal parameters. However, these approaches are designed based on a time-invariant system and singular value decomposition, which require a significant amount of computational time. Thus, these methods are difficult to employ for online identification. According to the adaptive filter theory, the recursive predictor-based subspace identification algorithm can not only avoid the singular value decomposition computation but also provide unbiased estimates in a general noisy framework using the recursive least squares approach. Furthermore, in comparison with the classical projection approximation subspace tracking series recursive algorithm, the recursive predictor-based subspace identification method is more suitable for systems with strong noise disturbances. By establishing the dynamics model of a large rigid-flexible coupling spacecraft, three cases of on-orbit modal parameter variation with time are investigated, and the corresponding system frequencies are identified using the recursive predictor-based subspace identification, projection approximation subspace tracking, and singular value decomposition methods. The results demonstrate that the recursive predictor-based subspace identification algorithm can be used to effectively perform an online parameter identification, and the corresponding computational efficiency and noise robustness are better than those of the singular value decomposition and projection approximation subspace tracking series approaches, respectively. Finally, the applicability of this method is also verified through a numerical simulation.

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Modal parameter identification of structure under base excitation using vibration test data

Cited by 7 publications

References 8 publications

Analysis of Coupled Vibration Characteristics of Linear-Angular and Parameter Identification

Analysis of Coupled Vibration Characteristics of Linear-Angular and Parameter Identification

Kalman filter identification method for micro-vibration transfer paths of satellites

Identification of the time-varying modal parameters of a spacecraft with flexible appendages using a recursive predictor-based subspace identification algorithm

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