A Time-Domain Method for Establishing Modal Parameters and Flutter Margins From Space Shuttle Flight Data

1984 ◽  
Author(s):  
L. V. Andrew ◽  
C. C. Park
Keyword(s):  
Time Domain ◽  
Space Shuttle ◽  
Time Domain Method ◽  
Modal Parameters ◽  
Flight Data ◽  
Domain Method

Time Domain Parameter Identification Methods for Linear Modal Analysis: A Unifying Approach

1986 ◽  
Vol 108 (1) ◽  
pp. 1-8 ◽  
Author(s):  
J. M. Leuridan ◽  
D. L. Brown ◽  
R. J. Allemang
Keyword(s):  
Time Domain ◽  
Multiple Input Multiple Output ◽  
Time Domain Method ◽  
Modal Parameters ◽  
Vibration Data ◽  
Domain Method ◽  
Free Decay ◽  
Input Multiple Output ◽  
Modes Of Vibration ◽  
Exponential Method

The paper describes a method that uses a multivariate model in the form of a nonhomogeneous finite difference equation to identify modal parameters of a mechanical structure. The modal parameters of this equation are estimable using a model that involves multiple input, multiple output vibration data. Thus, improved global estimates of modal parameters can be obtained, including the identification of highly coupled and pseudo-repeated modes of vibration. When the data are in the form of impulse or free decay responses, then the parameters of the homogeneous part of the equation can be estimated separately, and the method is then related to the Least Squares Complex Exponential method, the Polyreference Time Domain method and the Ibrahim Time Domain method.

A Time-Domain Method for Identifying Modal Parameters

1986 ◽  
Vol 53 (1) ◽  
pp. 28-32 ◽  
Author(s):  
Zhen-ni Wang ◽  
Tong Fang
Keyword(s):  
Time Domain ◽  
Arma Model ◽  
Time Domain Method ◽  
Least Square ◽  
Ar Model ◽  
Modal Parameters ◽  
System Response ◽  
Domain Method ◽  
Linear Least Square ◽  
Digital Simulations

A time-domain method for identifying the modal parameters of a vibration system is presented. It is shown that system eigenvectors can be effectively estimated through the multivariate AR model representation of the system response to white noise excitation. In contrast to the usual ARMA model approach, in this method only a linear least square algorithm is required, so that a great amount of calculation is saved. Results of digital simulations support the identification method.

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