Automatic Bayesian modal identification method for structures based on blind source separation

2021 ◽  
pp. 1-15
Author(s):  
Liang Su ◽  
Jing-Quan Zhang ◽  
Yu-Nan Tang ◽  
Xin Huang
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Modal Identification ◽  
Identification Method

scholarly journals An Underdetermined Blind Source Separation Method with Application to Modal Identification

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Gang Yu
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Modal Identification ◽  
Modal Parameters ◽  
Underdetermined Blind Source Separation ◽  
Structural Dynamic ◽  
Time Frequency ◽  
Modal Response ◽  
Experimental Validations ◽  
Straight Lines

In structural dynamic analysis, the blind source separation (BSS) technique has been accepted as one of the most effective ways for modal identification, in which how to extract the modal parameters using very limited sensors is a highly challenging task in this field. In this paper, we first review the drawbacks of the conventional BSS methods and then propose a novel underdetermined BSS method for addressing the modal identification with limited sensors. The proposed method is established on the clustering features of time-frequency (TF) transform of modal response signals. This study finds that the TF energy belonging to different monotone modals can cluster into distinct straight lines. Meanwhile, we provide the detailed theorem to explain the clustering features. Moreover, the TF coefficients of each modal are employed to reconstruct all monotone signals, which can benefit to individually identify the modal parameters. In experimental validations, two experimental validations demonstrate the effectiveness of the proposed method.

Modal Identification Using Blind Source Separation Based on Moving Average Method

2013 ◽  
Vol 378 ◽  
pp. 375-381
Author(s):  
Jian Hua Du ◽  
Hong Wu Huang ◽  
Dian Dian Lan
Keyword(s):  
Blind Source Separation ◽  
Average Method ◽  
Moving Average ◽  
Source Separation ◽  
Modal Identification ◽  
Separation Performance ◽  
Identification Algorithm ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
The Stability

The paper discusses the basic principles of blind source separation and modal identification of structures, analyses the feasibility that using blind source separation techniques for modal parameter identification. According to the noisy features of the measured data in experiments, a second-order blind identification algorithm based on moving average method is proposed. By moving average method the noises are efficiently eliminated. It greatly improves the separation performance of this algorithm. The cantilever experiments verify the stability and the applicability of the algorithm.

A frequency domain blind identification method for operational modal analysis using a limited number of sensors

2020 ◽  
Vol 26 (17-18) ◽  
pp. 1383-1398
Author(s):  
Xinhui Li ◽  
Jerome Antoni ◽  
Michael J Brennan ◽  
Tiejun Yang ◽  
Zhigang Liu
Keyword(s):  
Modal Analysis ◽  
Frequency Domain ◽  
Blind Source Separation ◽  
Source Separation ◽  
Operating Conditions ◽  
Operational Modal Analysis ◽  
Modal Identification ◽  
Frequency Range ◽  
Vibration Data ◽  
Spatial Aliasing

Operational modal analysis is an experimental modal analysis approach, which uses vibration data collected when the structure is under operating conditions. Amongst the methods for operational modal analysis, blind source separation–based methods have been shown to be efficient and powerful. The existing blind source separation modal identification methods, however, require the number of sensors to be at least equal to the number of modes in the frequency range of interest to avoid spatial aliasing. In this article, a frequency domain algorithm that overcomes this problem is proposed, which is based on the joint diagonalization of a set of weighted covariance matrices. In the proposed approach, the frequency range of interest is partitioned into several frequency ranges in which the number of active modes in each band is less than the number of sensors. Numerical simulations and an experimental example demonstrate the efficacy of the method.

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