Blind Source Separation of Post-nonlinear Mixtures Using Evolutionary Computation and Order Statistics

2006 ◽  
pp. 66-73 ◽  
Author(s):  
Leonardo Tomazeli Duarte ◽  
Ricardo Suyama ◽  
Romis Ribeiro de Faissol Attux ◽  
Fernando José Von Zuben ◽  
João Marcos Travassos Romano
Keyword(s):  
Evolutionary Computation ◽  
Order Statistics ◽  
Blind Source Separation ◽  
Source Separation ◽  
Nonlinear Mixtures

scholarly journals A second-order statistics method for blind source separation in post-nonlinear mixtures

2019 ◽  
Vol 155 ◽  
pp. 63-72 ◽  
Author(s):  
Denis G. Fantinato ◽  
Leonardo T. Duarte ◽  
Yannick Deville ◽  
Romis Attux ◽  
Christian Jutten ◽  
...  
Keyword(s):  
Order Statistics ◽  
Blind Source Separation ◽  
Source Separation ◽  
Second Order ◽  
Second Order Statistics ◽  
Nonlinear Mixtures

scholarly journals Nonlinear Blind Source Separation and Fault Feature Extraction Method for Mining Machine Diagnosis

2019 ◽  
Vol 9 (9) ◽  
pp. 1852 ◽  
Author(s):  
Hua Ding ◽  
Yiliang Wang ◽  
Zhaojian Yang ◽  
Olivia Pfeiffer
Keyword(s):  
Blind Source Separation ◽  
Additive Noise ◽  
Rbf Neural Network ◽  
Source Separation ◽  
Mining Machine ◽  
Vibration Source ◽  
Vibration Signals ◽  
Feature Extraction Method ◽  
Strongly Nonlinear ◽  
Nonlinear Mixtures

Mining machines are strongly nonlinear systems, and their transmission vibration signals are nonlinear mixtures of different kinds of vibration sources. In addition, vibration signals measured by the accelerometer are contaminated by noise. As a result, it is inefficient and ineffective for the blind source separation (BSS) algorithm to separate the critical independent sources associated with the transmission fault vibrations. For this reason, a new method based on wavelet de-noising and nonlinear independent component analysis (ICA) is presented in this paper to tackle the nonlinear BSS problem with additive noise. The wavelet de-noising approach was first employed to eliminate the influence of the additive noise in the BSS procedure. Then, the radial basis function (RBF) neural network combined with the linear ICA was applied to the de-noised vibration signals. Vibration sources involved with the machine faults were separated. Subsequently, wavelet package decomposition (WPD) was used to extract distinct fault features from the source signals. Lastly, an RBF classifier was used to recognize the fault patterns. Field data acquired from a mining machine was used to evaluate and validate the proposed diagnostic method. The experimental analysis results show that critical fault vibration source component can be separated by the proposed method, and the fault detection rate is superior to the linear ICA based approaches.

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