On-line modal parameter monitoring of bridges exploiting multi-core capacity by recursive stochastic subspace identification method

2008 ◽  
Author(s):  
Ping Lin ◽  
Nanxiong Zhang ◽  
Bin Ni
Keyword(s):  
Subspace Identification ◽  
Modal Parameter ◽  
Stochastic Subspace Identification ◽  
Identification Method ◽  
On Line ◽  
Subspace Identification Method ◽  
Parameter Monitoring

A Modified Stochastic Subspace Identification Method for Estimating Natural Frequencies

2016 ◽  
Author(s):  
Kaoshan Dai ◽  
Ying Wang ◽  
Yichao Huang ◽  
W. D. Zhu ◽  
Y. F. Xu
Keyword(s):  
Natural Frequencies ◽  
Health Assessment ◽  
Operating Conditions ◽  
Subspace Identification ◽  
Lumped Mass ◽  
Mass System ◽  
Stochastic Subspace Identification ◽  
Identification Method ◽  
Periodic Rotation ◽  
Subspace Identification Method

A system identification method for estimating natural frequencies is proposed. This method developed based on the stochastic subspace identification method can identify modal parameters of structures in operating conditions with harmonic components in excitation. It benefits wind turbine tower structural health assessment because classical operational modal analysis methods can fail as periodic rotation excitation from a turbine introduces strong harmonic disturbance to tower structure response data. The effectiveness, accuracy and robustness of the proposed method were numerically investigated and verified through a lumped-mass system model.

A Modal Parameter Identification Method Based on Improved Covariance-Driven Stochastic Subspace Identification

2020 ◽  
Vol 142 (6) ◽  
Author(s):  
Chen Wang ◽  
Minghui Hu ◽  
Zhinong Jiang ◽  
Yanfei Zuo ◽  
Zhenqiao Zhu
Keyword(s):  
Parameter Identification ◽  
Gas Turbines ◽  
Excitation Frequency ◽  
Modal Parameters ◽  
Subspace Identification ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Mode Identification ◽  
Stochastic Subspace Identification ◽  
Identification Method

Abstract For the quantitative dynamic analysis of aero gas turbines, accurate modal parameters must be identified. However, the complicated structure of thin-walled casings may cause false mode identification and mode absences if conventional methods are used, which makes it more difficult to identify the modal parameters. A modal parameter identification method based on improved covariance-driven stochastic subspace identification (covariance-driven SSI) is proposed. The ability to reduce the number of mode absences and the solving stability are improved by a covariance matrix dimension control method. Meanwhile, the number of false mode identification is reduced via a false mode elimination method. In addition, the real mode complementation and the excitation frequency mode screening can be realized by a multispeed excitation method. The numerical results of a typical rotor model and measured data of an aero gas turbine validated the proposed method.

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