An Alternating Least Squares (ALS) based Blind Source Separation Algorithm for Operational Modal Analysis

2011 ◽  
pp. 179-187 ◽  
Author(s):  
J. Antoni ◽  
S. Chauhan
Keyword(s):  
Least Squares ◽  
Modal Analysis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Operational Modal Analysis ◽  
Alternating Least Squares ◽  
Separation 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.

VIBRATORY BEHAVIOR OF A DOUBLE PANEL SYSTEM BY THE OPERATIONAL MODAL ANALYSIS

2011 ◽  
Vol 02 (04) ◽  
pp. 459-479 ◽  
Author(s):  
MOHAMED SLIM ABBES ◽  
MARIEM MILADI CHAABANE ◽  
ALI AKROUT ◽  
TAHAR FAKHFAKH ◽  
MOHAMED HADDAR
Keyword(s):  
Independent Component Analysis ◽  
Modal Analysis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Component Analysis ◽  
Operational Modal Analysis ◽  
Independent Component ◽  
Good Argument ◽  
Experimental Works

The present study tackles the vibratory behavior of a double panel system by operational modal analysis (OMA) using one of the major techniques of blind source separation (BSS), which is the independent component analysis (ICA). For this purpose, the OMA method and the ICA concept are presented and exploited in order to identify the eigenmodes of a double panel system. Then, results obtained by the OMA technique are presented and compared with those achieved by the modal recombination method. Since a good argument is observed, this approach can be used in conjunction with experimental works.

scholarly journals Constrained Blind Source Separation by Morphological Characteristics and Its Application in Modal Analysis

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Teng Gong ◽  
Zhousuo Zhang ◽  
Huan Wang
Keyword(s):  
Modal Analysis ◽  
Blind Source Separation ◽  
Prior Information ◽  
Source Separation ◽  
Morphological Characteristics ◽  
Separation Algorithm ◽  
Contrast Function ◽  
Optimization Calculation ◽  
Source Signal ◽  
Source Signals

Semi-blind source separation algorithm is widely concerned for its advantages over classical blind source separation algorithm. However, in practical applications, it is often a difficult problem to design reference signals, which should be closely related to the desired source signals. Therefore the algorithm of constrained blind source separation by morphological characteristics is proposed in this paper, including three steps: the establishment of the enhanced contrast function, the optimization calculation and the extraction of multiple source signals. Firstly, the indexes measuring the morphological characteristics of a source signal are constructed based on the known prior information and introduced into the traditional contrast function to establish an enhanced contrast function, extending the use of prior information. Then, the optimization calculation is accomplished by genetic algorithm, obtaining a single source signal. Finally, the extraction of multiple source signals is realized by cluster analysis. The proposed algorithm is applied to the modal analysis under random excitation. The spectrum symmetry index is constructed and introduced into the kurtosis contrast function to establish the enhanced contrast function, thus realizing the extraction of each signal modal response. The extraction results show the effectiveness and superiority of the algorithm.

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