Research on Modal Identification of Structures Using Robust Second-Order Blind Identification Technique

2013 ◽  
Vol 389 ◽  
pp. 712-720
Author(s):  
Jian Hua Du ◽  
Hong Wu Huang ◽  
Dian Dian Lan
Keyword(s):  
Second Order ◽  
Modal Identification ◽  
Mode Shapes ◽  
Blind Identification ◽  
Separation Algorithm ◽  
Single Degree Of Freedom ◽  
Second Order Statistics ◽  
Single Degree ◽  
Identification Technique ◽  
Simulation Results

The paper discusses the basic principle of blind source separation algorithm applying in structural modal identification. By improving the signal-whitening method, a robust second-order blind identification (RSOBI) algorithm is established on the basis of second-order statistics. The modal responses and mode shapes can be obtained using the RSOBI algorithm from the observed data of structures in time domain. Frequency and damping are estimated from the modal responses by traditional single degree of freedom methods. The simulation results show that the RSOBI algorithm has good performance in modal identification of structures.

A New Blind Source Separation Algorithm in Application of Over-Determined Mode under Lower SNR

2010 ◽  
Vol 139-141 ◽  
pp. 2132-2135
Author(s):  
Ping Wang ◽  
Jian Chen ◽  
Ji Xiang Lu
Keyword(s):  
Time Delay ◽  
Order Statistics ◽  
Blind Source Separation ◽  
Correlation Matrix ◽  
Second Order ◽  
Blind Identification ◽  
Separation Algorithm ◽  
Communication Signals ◽  
Second Order Statistics ◽  
Modulation Methods

The paper proposes a new BSS algorithm based on the second-order statistics. By jointly diagonalizing the time delay correlation matrix of the observed signals and using the improved new non-orthogonal joint diagonalization (NOJD) method, a better solution is achieved. The proposed algorithm can successfully separate communication signals under SNR as low as 10dB and the over-determined mode regardless of the signals’ modulation methods. Signal to Interference Noise Ratio (SINR) is used to prove the superiority of the proposed algorithm over the classical Second-Order Blind Identification (SOBI).

Extending Blind Modal Identification to the Underdetermined Case for Ambient Vibration

2012 ◽  
Author(s):  
Scot McNeill
Keyword(s):  
Natural Frequencies ◽  
Second Order ◽  
Modal Identification ◽  
Ambient Vibration ◽  
Mode Shapes ◽  
Frequency Domain Method ◽  
Blind Identification ◽  
Vibration Data ◽  
Modal Damping ◽  
Six Dof

The modal identification framework known as Blind Modal Identification (BMID) has recently been developed, drawing on techniques from Blind Source Separation (BSS). Therein, a BSS algorithm known as Second Order Blind Identification (SOBI) was adapted to solve the Modal IDentification (MID) problem. One of the drawbacks of the technique is that the number of modes identified must be less than the number of sensors used to measure the vibration of the equipment or structure. In this paper, an extension of the BMID method is presented for the underdetermined case, where the number of sensors is less than the number of modes to be identified. The analytic signal formed from measured vibration data is formed and the Second Order Blind Identification of Underdetermined Mixtures (SOBIUM) algorithm is applied to estimate the complex-valued modes and modal response autocorrelation functions. The natural frequencies and modal damping ratios are then estimated from the corresponding modal auto spectral density functions using a simple Single Degree Of Freedom (SDOF), frequency-domain method. Theoretical limitations on the number of modes identified given the number of sensors are provided. The method is demonstrated using a simulated six DOF mass-spring-dashpot system excited by white noise, where displacement at four of the six DOF is measured. All six modes are successfully identified using data from only four sensors. The method is also applied to a more realistic simulation of ambient building vibration. Seven modes in the bandwidth of interest are successfully identified using acceleration data from only five DOF. In both examples, the identified modal parameters (natural frequencies, mode shapes, modal damping ratios) are compared to the analytical parameters and are demonstrated to be of good quality.

Vector Triggering Random Decrement for High Identification Accuracy

1998 ◽  
Vol 120 (4) ◽  
pp. 970-975 ◽  
Author(s):  
S. R. Ibrahim ◽  
J. C. Asmussen ◽  
R. Brincker
Keyword(s):  
Time Domain ◽  
Modal Identification ◽  
Identification Accuracy ◽  
Mode Shapes ◽  
Phase Information ◽  
Free Response ◽  
Identification Methods ◽  
Random Decrement ◽  
Identification Technique ◽  
Triggering Conditions

Using the Random Decrement (RD) technique to obtain free response estimates and combining this with time domain modal identification methods to obtain the poles and the mode shapes is acknowledged as a fast and accurate way of analysing measured responses of structures subject to ambient loads. When commonly accepted triggering conditions are used however, the user is restricted to use a combination of auto RD and cross RD functions with high noise contents on the cross RD functions. Use of the auto RD functions alone causes the loss of phase information and thus the possibility of estimating mode shapes. In this paper a new algorithm based on pure auto triggering is suggested. Equivalent auto RD functions are estimated for all channels to obtain functions with a minimum of noise, using a vector triggering condition that preserves phase information, and thus, allows for estimation of both poles and mode shapes. The proposed technique (VRD) is compared with the traditional RD technique by evaluating modal parameters extracted from the RD and the VRD functions using ITD identification technique on simulated and experimentally obtained data.

Iterative Learning Control for a Class of Modeling Undesirable Single Degree of Freedom System

2014 ◽  
Vol 538 ◽  
pp. 379-382
Author(s):  
Wei Zhou ◽  
Bao Bin Liu
Keyword(s):  
Iterative Learning Control ◽  
Learning Algorithm ◽  
Learning Control ◽  
Iterative Learning ◽  
Degree Of Freedom ◽  
Control Input ◽  
Convergence Properties ◽  
Single Degree Of Freedom ◽  
Single Degree ◽  
Simulation Results

A class of modeling undesirable single degree of freedom system is studied by using iterative learning control. The proposed iterative learning algorithm constantly updates the control input according to output error until the desired output occurred. So the system with designed controller can achieve perfect accuracy. We have proved convergence properties in iteration domain and simulation results demonstrate the effectiveness of the presented method.

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