Increasing the efficiency and efficacy of second-order blind identification (SOBI) methods

2016 ◽  
Vol 24 (6) ◽  
pp. e1921 ◽  
Author(s):  
P. T. Brewick ◽  
A. W. Smyth
Keyword(s):  
Second Order ◽  
Blind Identification

scholarly journals Fault Diagnosis of Rotating Machinery Based on Multi-Sensor Signals and Median Filter Second-Order Blind Identification (MF-SOBI)

2020 ◽  
Vol 10 (11) ◽  
pp. 3735 ◽  
Author(s):  
Feng Miao ◽  
Rongzhen Zhao ◽  
Leilei Jia ◽  
Xianli Wang
Keyword(s):  
Blind Source Separation ◽  
Impulse Noise ◽  
Median Filter ◽  
Source Separation ◽  
Rotating Machinery ◽  
Second Order ◽  
Time Signal ◽  
Blind Identification ◽  
Sensor Signals ◽  
Strong Impulse

Feature extraction plays a crucial role in the diagnosis of rotating machinery faults. However, the vibration signals measured are inherently complex and non-stationary and the features of faulty signals are often submerged by noise. The principle and method of blind source separation are introduced, and we point out that the blind source separation algorithm is invalid in an environment of strong impulse noise. In order to solve the problem of fast separation of multi-sensor signals in an environment of strong impulse noise, first, the window width of the median filter (MF) is calculated according to the sampling frequency, so that the impulse noise and part of the white noise can be effectively filtered out. Next, the filtered signals are separated by the improved second-order blind identification (SOBI) algorithm. At the same time, the method is tested on the strong pulse background noise and rub impact dataset. The results show that this method has higher efficiency and accuracy than the direct separation method. It is possible to apply the method to real-time signal analysis due to its speed and efficiency.

Research on Modal Identification of Structures Using Robust Second-Order Blind Identification Technique

2013 ◽  
Vol 389 ◽  
pp. 712-720
Author(s):  
Jian Hua Du ◽  
Hong Wu Huang ◽  
Dian Dian Lan
Keyword(s):  
Second Order ◽  
Modal Identification ◽  
Mode Shapes ◽  
Blind Identification ◽  
Separation Algorithm ◽  
Single Degree Of Freedom ◽  
Second Order Statistics ◽  
Single Degree ◽  
Identification Technique ◽  
Simulation Results

The paper discusses the basic principle of blind source separation algorithm applying in structural modal identification. By improving the signal-whitening method, a robust second-order blind identification (RSOBI) algorithm is established on the basis of second-order statistics. The modal responses and mode shapes can be obtained using the RSOBI algorithm from the observed data of structures in time domain. Frequency and damping are estimated from the modal responses by traditional single degree of freedom methods. The simulation results show that the RSOBI algorithm has good performance in modal identification of structures.

A New Blind Source Separation Algorithm in Application of Over-Determined Mode under Lower SNR

2010 ◽  
Vol 139-141 ◽  
pp. 2132-2135
Author(s):  
Ping Wang ◽  
Jian Chen ◽  
Ji Xiang Lu
Keyword(s):  
Time Delay ◽  
Order Statistics ◽  
Blind Source Separation ◽  
Correlation Matrix ◽  
Second Order ◽  
Blind Identification ◽  
Separation Algorithm ◽  
Communication Signals ◽  
Second Order Statistics ◽  
Modulation Methods

The paper proposes a new BSS algorithm based on the second-order statistics. By jointly diagonalizing the time delay correlation matrix of the observed signals and using the improved new non-orthogonal joint diagonalization (NOJD) method, a better solution is achieved. The proposed algorithm can successfully separate communication signals under SNR as low as 10dB and the over-determined mode regardless of the signals’ modulation methods. Signal to Interference Noise Ratio (SINR) is used to prove the superiority of the proposed algorithm over the classical Second-Order Blind Identification (SOBI).

Extending Blind Modal Identification to the Underdetermined Case for Ambient Vibration

2012 ◽  
Author(s):  
Scot McNeill
Keyword(s):  
Natural Frequencies ◽  
Second Order ◽  
Modal Identification ◽  
Ambient Vibration ◽  
Mode Shapes ◽  
Frequency Domain Method ◽  
Blind Identification ◽  
Vibration Data ◽  
Modal Damping ◽  
Six Dof

The modal identification framework known as Blind Modal Identification (BMID) has recently been developed, drawing on techniques from Blind Source Separation (BSS). Therein, a BSS algorithm known as Second Order Blind Identification (SOBI) was adapted to solve the Modal IDentification (MID) problem. One of the drawbacks of the technique is that the number of modes identified must be less than the number of sensors used to measure the vibration of the equipment or structure. In this paper, an extension of the BMID method is presented for the underdetermined case, where the number of sensors is less than the number of modes to be identified. The analytic signal formed from measured vibration data is formed and the Second Order Blind Identification of Underdetermined Mixtures (SOBIUM) algorithm is applied to estimate the complex-valued modes and modal response autocorrelation functions. The natural frequencies and modal damping ratios are then estimated from the corresponding modal auto spectral density functions using a simple Single Degree Of Freedom (SDOF), frequency-domain method. Theoretical limitations on the number of modes identified given the number of sensors are provided. The method is demonstrated using a simulated six DOF mass-spring-dashpot system excited by white noise, where displacement at four of the six DOF is measured. All six modes are successfully identified using data from only four sensors. The method is also applied to a more realistic simulation of ambient building vibration. Seven modes in the bandwidth of interest are successfully identified using acceleration data from only five DOF. In both examples, the identified modal parameters (natural frequencies, mode shapes, modal damping ratios) are compared to the analytical parameters and are demonstrated to be of good quality.

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