Automatic Modal Parameter Identification of Cable-Stayed Bridge Based on the Stochastic Subspace Identification Method

2020 ◽  
pp. 321-329
Author(s):  
Liao Yuchen ◽  
Zhang Kun ◽  
Zong Zhouhong ◽  
Lin Dinan
Keyword(s):  
Parameter Identification ◽  
Subspace Identification ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Stochastic Subspace Identification ◽  
Cable Stayed Bridge ◽  
Identification Method ◽  
Subspace Identification Method

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.

scholarly journals Modal Parameter Identification of Structures Using Reconstructed Displacements and Stochastic Subspace Identification

2021 ◽  
Vol 11 (23) ◽  
pp. 11432
Author(s):  
Xiangying Guo ◽  
Changkun Li ◽  
Zhong Luo ◽  
Dongxing Cao
Keyword(s):  
Parameter Identification ◽  
Modal Analysis ◽  
Moving Average ◽  
Random Fluctuation ◽  
High Frequency Component ◽  
Modal Parameters ◽  
Subspace Identification ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Stochastic Subspace Identification

A method of modal parameter identification of structures using reconstructed displacements was proposed in the present research. The proposed method was developed based on the stochastic subspace identification (SSI) approach and used reconstructed displacements of measured accelerations as inputs. These reconstructed displacements suppressed the high-frequency component of measured acceleration data. Therefore, in comparison to the acceleration-based modal analysis, the operational modal analysis obtained more reliable and stable identification parameters from displacements regardless of the model order. However, due to the difficulty of displacement measurement, different types of noise interferences occurred when an acceleration sensor was used, causing a trend term drift error in the integral displacement. A moving average low-frequency attenuation frequency-domain integral was used to reconstruct displacements, and the moving time window was used in combination with the SSI method to identify the structural modal parameters. First, measured accelerations were used to estimate displacements. Due to the interference of noise and the influence of initial conditions, the integral displacement inevitably had a drift term. The moving average method was then used in combination with a filter to effectively eliminate the random fluctuation interference in measurement data and reduce the influence of random errors. Real displacement results of a structure were obtained through multiple smoothing, filtering, and integration. Finally, using reconstructed displacements as inputs, the improved SSI method was employed to identify the modal parameters of the structure.

scholarly journals Modal parameters identification of bridge by improved stochastic subspace identification method with Grubbs criterion

2021 ◽  
Vol 54 (3-4) ◽  
pp. 457-464
Author(s):  
Yulin Zhou ◽  
Xulei Jiang ◽  
Mingjin Zhang ◽  
Jinxiang Zhang ◽  
Hao Sun ◽  
...  
Keyword(s):  
Wind Tunnel ◽  
Parameter Identification ◽  
Damping Ratio ◽  
Wind Tunnel Test ◽  
Modal Parameters ◽  
Subspace Identification ◽  
Identification Algorithm ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Stochastic Subspace Identification

In the wind tunnel test of a long-span bridge model, to ensure that the dynamic characteristics of the model can satisfy the test design requirements, it is particularly important to accurately identify the modal parameters of the model. First, the stochastic subspace identification algorithm was used to analyze the modal parameters of the model in the wind tunnel test; then, Grubbs criterion was introduced to effectively eliminate outliers in the damping ratio matrix. Stochastic subspace identification algorithm with Grubbs criterion improved the accuracy of the modal parameter identification and the ability to determine system matrix order and prevented the modal omissions caused by determining the stable condition of the damping ratio in the stability diagram. Finally, Oujiang Bridge was used as an example to verify the stochastic subspace identification algorithm with Grubbs criterion and compare with the results of the finite element method. The example shows that the improved method can be effectively applied to the modal parameter identification of bridges.

scholarly journals Data Interpretation and Continuous Modal Parameter Identification of Cable Stayed Bridge

2015 ◽  
Vol 9 (1) ◽  
pp. 577-591 ◽  
Author(s):  
Inamullah Khan ◽  
Deshan Shan ◽  
Qiao Li ◽  
Jie He ◽  
Fei Long Nan
Keyword(s):  
Parameter Identification ◽  
Real Life ◽  
Data Interpretation ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Stochastic Subspace Identification ◽  
Cable Stayed Bridge ◽  
Long Span ◽  
Exploratory Data

For reliable identification of modal parameters, it is important to distinguish between abnormal data due to defects, malfunctioning, and anomalies in the sensors, from that of precise data. In case of long-term continuous monitoring data, it is imperative to identify any defects in the raw data very quickly and accurately to ensure that the identification is trustworthy. Exploratory Data Analysis (EDA) is employed for the purpose of quickly visualizing any defects and anomalies in the sensor’s data. Outlier analysis is employed to make some data treatment followed by auto and cross correlation to further elucidate any defects and anomalies in the collected data. Finally, covariance driven stochastic subspace identification (CO-SSI) with some improvements is employed to carry out the continuous modal parameter identification. The Sutong Yangtze river bridge, a long span Y-shape pylon cable stayed bridge with a main span of 1088m was chosen as a case study and the above proposed methods were applied. The result showed that the suggested method is very effective and can provide better and more accurate real life results in the continuous health monitoring of bridges.

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