Ambient modal identification of structures equipped with tuned mass dampers using parallel factor blind source separation

2014 ◽  
Vol 13 (2) ◽  
pp. 257-280 ◽  
Author(s):  
A. Sadhu ◽  
B. Hazraa ◽  
S. Narasimhan
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Modal Identification ◽  
Tuned Mass Dampers ◽  
Parallel Factor

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.

Tensor Factorization with Application to Convolutive Blind Source Separation of Speech

2010 ◽  
pp. 186-206
Author(s):  
Saeid Sanei ◽  
Bahador Makkiabadi
Keyword(s):  
Frequency Domain ◽  
Blind Source Separation ◽  
Source Separation ◽  
Real Data ◽  
Tensor Factorization ◽  
Major Application ◽  
Parallel Factor ◽  
New Formulation ◽  
Permutation Problems ◽  
Signal Sources

Tensor factorization (TF) is introduced as a powerful tool for solving multi-way problems. As an effective and major application of this technique, separation of sound particularly speech signal sources from their corresponding convolutive mixtures is described and the results are demonstrated. The method is flexible and can easily incorporate all possible parameters or factors into the separation formulation. As a consequence of that fewer assumptions (such as uncorrelatedness and independency) will be required. The new formulation allows further degree of freedom to the original parallel factor analysis (PARAFAC) problem in which the scaling and permutation problems of the frequency domain blind source separation (BSS) can be resolved. Based on the results of experiments using real data in a simulated medium, it has been concluded that compared to conventional frequency domain BSS methods, both objective and subjective results are improved when the proposed algorithm is used.

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