Modal Parameter Estimation of Time-varying Structures Using GSC-TARMA Models Based on Vector Vibration Response Measurements

被引：0
作者：
Yu L. [1 ,2 ]
Liu L. [1 ,2 ]
Ma Z. [3 ,4 ]
Kang J. [1 ,2 ]
机构：
[1] School of Aerospace and Engineering, Beijing Institute of Technology, Beijing
[2] Key Laboratory of Dynamics and Control of Flight Vehicle, Ministry of Education, Beijing Institute of Technology, Beijing
[3] School of Mechanical Engineering, Tianjin University, Tianjin
[4] Tianjin Key Laboratory of Nonlinear Dynamics and Control, Tianjin University, Tianjin
来源：
Jixie Gongcheng Xuebao/Journal of Mechanical Engineering | 2019年 / 55卷 / 15期
关键词：
Generalized stochastic constraints; Modal parameter estimation; Time-varying structures; Vector vibration responses;
D O I：
10.3901/JME.2019.15.183
中图分类号：
学科分类号：
摘要：
The problem of output-only identification of time-varying structures based on vector vibration response measurements is considered. A generalized stochastic constraints vector time-dependent auto-regressive moving average (GSC-VTARMA) model is presented, which is an extension form of the generalized stochastic constraints time-dependent ARMA (GSC-TARMA) model. In order to reduce computation complexity, an improved generalized stochastic constraints vector time-dependent auto-regressive moving average (GSC-TARMA*) model is subsequently proposed. The proposed model is validated by non-stationary vibration signals of a numerical system with time-varying stiffness and a laboratory time-varying structure consisting of a simply supported beam and a moving mass. The results indicate that the proposed GSC-VTARMA* model achieves same identification accuracy but less computation complexity to the GSC-VTARMA model, and achieves better identification robustness and higher data utilization to the GSC-TARMA model. Furthermore, the proposed model shows a similar identification accuracy and lower computational cost than the traditional FS-VTARMA model. Due to the recursive algorithm used by the GSC-VTARMA* model, its enhanced online identification capability has also been demonstrated. © 2019 Automation of Electric Power Systems Press.
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页码：183 / 192
页数：9
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