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.

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
Keyword(s):  
Online Monitoring ◽  
Rapid Development ◽  
Health Assessment ◽  
Field Tests ◽  
Mode Frequency ◽  
Subspace Identification ◽  
Monitoring Methods ◽  
Underground Engineering ◽  
Stochastic Subspace Identification ◽  
Identification Method

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.

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

2017 ◽  
Vol 24 (20) ◽  
pp. 4781-4796 ◽  
Author(s):  
Wenchao Li ◽  
Viet-Hung Vu ◽  
Zhaoheng Liu ◽  
Marc Thomas ◽  
Bruce Hazel
Keyword(s):  
Data Driven ◽  
Modal Parameters ◽  
Subspace Identification ◽  
Time Varying ◽  
Lumped Mass ◽  
State Matrix ◽  
Mass System ◽  
Stochastic Subspace Identification ◽  
Time Varying Systems ◽  
Using Data

This paper presents a method for the extraction of modal parameters for identification of time-varying systems using Data-Driven Stochastic Subspace Identification (SSI-DATA). In practical applications of SSI-DATA, both the modal parameters and computational ones are mixed together in the identified results. In order to differentiate the structural ones from computational ones, a new method based on the eigen-decomposition of the state matrix constructed in SSI-DATA is proposed. The efficiency of the proposed method is demonstrated through numerical simulation of a lumped-mass system and experimental test of a moving robot for extracting excited natural frequencies of the system.

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