Modal Parameter Identification of Time-Varying Systems Using the Time-Varying Multivariate Autoregressive Model

2005 ◽  
Author(s):  
Lilan Liu ◽  
Hongzhao Liu ◽  
Ziying Wu ◽  
Daning Yuan ◽  
Pengfei Li
Keyword(s):  
Parameter Identification ◽  
Linear Time ◽  
Damping Ratio ◽  
Least Square Method ◽  
Least Square ◽  
Time Varying ◽  
Recursive Least Square ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Multivariate Autoregressive

A new time-varying multivariate autoregressive (TVMAR) model method for modal parameter identification of linear time-varying (TV) systems with multi-output is introduced. Besides, a modified recursive least square method based on the traditional one is presented to determine the coefficient matrices of the TVMAR model. In the proposed method, multi-dimensional nonstationary response signals of the vibrating system can be processed simultaneously. Not only the TV modal frequency and damping ratio of the system, but also the changing behavior of the mode shape in the course of vibration are identified by the proposed procedure. Numerical simulations, in which a three-degree-of-freedom system with TV stiffness is respectively subjected to impulse excitation and white noise excitation, are presented. The validity and accuracy of the method are demonstrated by the good simulation results.

A Method for Modal Parameter Identification of Time Varying Vibration Systems

2012 ◽  
Vol 479-481 ◽  
pp. 688-693
Author(s):  
Zi Ying Wu ◽  
Kun Shi
Keyword(s):  
Linear Time ◽  
Least Square Method ◽  
Degree Of Freedom ◽  
Least Square ◽  
Modal Parameters ◽  
Time Varying ◽  
Recursive Least Square ◽  
Modal Parameter ◽  
Linear Time Invariant ◽  
Prony Method

In this paper a new time varying multivariate Prony (TVM-Prony) method is put forward to identify modal parameters of time varying (TV) multiple-degree-of-freedom systems from measured vibration responses. The proposed method is based on the classical Prony method that is often used to identify modal parameters of linear time invariant systems. The main advantage of the propose approach is that it can analyze multi-dimensional nonstationary signals simultaneously. A modified recursive least square method based on the traditional one is presented to determine the TV coefficient matrices of the multivariate parametric model established in the proposed method. The efficiency and accuracy of the identification approach is demonstrated by a numerical example, in which a TV mass-string system with three-degree-of-freedom is investigated. Satisfied results are obtained.

scholarly journals Instantaneous Modal Parameter Identification of Linear Time-Varying Systems Based on Chirplet Adaptive Decomposition

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Jie Zhang ◽  
Zhiyu Shi
Keyword(s):  
Parameter Identification ◽  
Energy Analysis ◽  
Linear Time ◽  
Damping Ratio ◽  
Energy Concentration ◽  
Time Varying ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Time Frequency ◽  
Time Varying Systems

Instantaneous modal parameter identification of time-varying dynamic systems is a useful but challenging task, especially in the identification of damping ratio. This paper presents a method for modal parameter identification of linear time-varying systems by combining adaptive time-frequency decomposition and signal energy analysis. In this framework, the adaptive linear chirplet transform is applied in time-frequency analysis of acceleration response for its higher energy concentration, and the response of each mode can be adaptively decomposed via an adaptive Kalman filter. Then, the damping ratio of the time-varying systems is identified based on energy analysis of component response signal. The proposed method can not only improve the accuracy of instantaneous frequency extraction but also ensure the antinoise ability in identifying the damping ratio. The efficiency of the method is first verified through a numerical simulation of a three-degree-of-freedom time-varying structure. Then, the method is demonstrated by comparing with the traditional wavelet and time-domain peak method. The identified results illustrate that the proposed method can obtain more accurate modal parameters in low signal-to-noise ratio (SNR) scenarios.

scholarly journals Time-Varying Modal Parameters Identification by Subspace Tracking Algorithm and Its Validation Method

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Haotian Zhou ◽  
Kaiping Yu ◽  
Yushu Chen ◽  
Rui Zhao ◽  
Yunhe Bai
Keyword(s):  
Parameter Identification ◽  
Spatial Clustering ◽  
Signal To Noise Ratio ◽  
Information Criterion ◽  
Modal Parameters ◽  
Identification Algorithm ◽  
Time Varying ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Post Process

This article presents a time-varying modal parameter identification method based on the novel information criterion (NIC) algorithm and a post-process method for time-varying modal parameter estimation. In the practical application of the time-varying modal parameter identification algorithm, the identified results contain both real modal parameters and aberrant ones caused by the measurement noise. In order to improve the quality of the identified results as well as sifting and validating the real modal parameters, a post-process procedure based on density-based spatial clustering of applications with noise (DBSCAN) algorithm is introduced. The efficiency of the proposed approach is first verified through a numerical simulation of a cantilever Euler-Bernoulli beam with a time-varying mass. Then the proposed approach is experimentally demonstrated by composite sandwich structure in a time-varying high temperature environment. The identified results illustrate that the proposed approach can obtain real modal frequencies in low signal-to-noise ratio (SNR) scenarios.

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