scholarly journals The Tunnel Structural Mode Frequency Characteristics Identification and Analysis Based on a Modified Stochastic Subspace Identification Method

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Biao Zhou ◽  
Xiongyao Xie ◽  
Xiaojian Wang

With the rapid development of underground engineering in China, the heavy structural maintenance work followed is expected to be a great challenge in the future. The development also provides a promising application prospect for the newly developed vibration-based health assessment and monitoring methods. However, the fact that tunnels are embedded in soil makes collecting and identifying the vibration characteristics more difficult, especially for the online monitoring. In this paper, a new identification method that combines the natural excitation technique (NExT) and stochastic subspace identification (SSI) method is developed. The new method is compared with the traditional SSI method, and mode frequency analysis is made based on a series of field tests carried out at the subway and power tunnel. It is found that both stability and efficiency of the mode frequency identification have been greatly improved, and it more suitable for online monitoring. Meanwhile, a mathematical model is used to analyze the original mode characteristics and the influence of soil coupling. The results are also compared with the field tests results by using the NExT-SSI method, and some recommendations are also made for how to choose the vibration modals for vibration-based monitoring in the tunnel.

Author(s):  
Kaoshan Dai ◽  
Ying Wang ◽  
Yichao Huang ◽  
W. D. Zhu ◽  
Y. F. Xu

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.


2020 ◽  
Vol 142 (6) ◽  
Author(s):  
Chen Wang ◽  
Minghui Hu ◽  
Zhinong Jiang ◽  
Yanfei Zuo ◽  
Zhenqiao Zhu

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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