A comparative study of feature extraction and blind source separation of independent component analysis (ICA) on childhood brain tumour 1 H magnetic resonance spectra

2009 ◽  
Vol 22 (8) ◽  
pp. 809-818 ◽  
Author(s):  
Jie Hao ◽  
Xin Zou ◽  
Martin P. Wilson ◽  
Nigel P. Davies ◽  
Yu Sun ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Magnetic Resonance ◽  
Independent Component Analysis ◽  
Comparative Study ◽  
Brain Tumour ◽  
Blind Source Separation ◽  
Source Separation ◽  
Component Analysis ◽  
Independent Component ◽  
Magnetic Resonance Spectra

Application of Independent Component Analysis in Machine Fault Diagnosis

2014 ◽  
Vol 905 ◽  
pp. 524-527
Author(s):  
Feng Miao ◽  
Rong Zhen Zhao
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Independent Component Analysis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Learning Rule ◽  
Component Analysis ◽  
Independent Component ◽  
Machine Fault Diagnosis ◽  
Machine Fault

A novel fast algorithm for lndependent Component Analysis is introduced, which can be used for blind source separation and machine fault diagnosis feature extraction. It is shown how a neural network learning rule can be transformed into a fixed-point iteration, which provides an algorithm that is very simple, does not depend on any user-defined parameters, and is fast to converge to the most accurate solution allowed by the data. The purpose of this paper is to review the application of blind source separation in the machine fault diagnosis,including the following aspects: noise elimination and extraction of the weak signals,the separation of multi-fault sources,redundancy reduction,feature extraction and pattern classification based on independent component analysis. And its application in machine fault diagnosis is illustrated by the examples. In addition, some prospects about using blind source separation for machine fault diagnosis are discussed.

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