Blind source separation with time series variational Bayes expectation maximization algorithm

2012 ◽  
Vol 22 (1) ◽  
pp. 17-33 ◽  
Author(s):  
Shijun Sun ◽  
Chenglin Peng ◽  
Wensheng Hou ◽  
Jun Zheng ◽  
Yingtao Jiang ◽  
...  
Keyword(s):  
Time Series ◽  
Blind Source Separation ◽  
Expectation Maximization ◽  
Source Separation ◽  
Expectation Maximization Algorithm ◽  
Variational Bayes

scholarly journals Blind source separation problem in GPS time series

2015 ◽  
Vol 90 (4) ◽  
pp. 323-341 ◽  
Author(s):  
A. Gualandi ◽  
E. Serpelloni ◽  
M. E. Belardinelli
Keyword(s):  
Time Series ◽  
Blind Source Separation ◽  
Source Separation ◽  
Separation Problem ◽  
Gps Time Series

scholarly journals A Novel Damage Detection Algorithm using Time-Series Analysis-Based Blind Source Separation

2013 ◽  
Vol 20 (3) ◽  
pp. 423-438 ◽  
Author(s):  
A. Sadhu ◽  
B. Hazra
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Damage Detection ◽  
Blind Source Separation ◽  
Source Separation ◽  
Damage Index ◽  
Detection Algorithm ◽  
Mode Shapes ◽  
Series Analysis ◽  
Modal Response

In this paper, a novel damage detection algorithm is developed based on blind source separation in conjunction with time-series analysis. Blind source separation (BSS), is a powerful signal processing tool that is used to identify the modal responses and mode shapes of a vibrating structure using only the knowledge of responses. In the proposed method, BSS is first employed to estimate the modal response using the vibration measurements. Time-series analysis is then performed to characterize the mono-component modal responses and successively the resulting time-series models are utilized for one-step ahead prediction of the modal response. With the occurrence of newer measurements containing the signature of damaged system, a variance-based damage index is used to identify the damage instant. Once the damage instant is identified, the damaged and undamaged modal parameters of the system are estimated in an adaptive fashion. The proposed method solves classical damage detection issues including the identification of damage instant, location as well as the severity of damage. The proposed damage detection algorithm is verified using extensive numerical simulations followed by the full scale study of UCLA Factor building using the measured responses under Parkfield earthquake.

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