Feature Extraction and Recognition of Ventilator Vibration Signal Based on ICA/SVM

Ventilator vibration signal is usually mixed with some signals and shows strong nonlinearity, nonstationarity and nonGaussian. It presents a great challenge to feature extraction and recognition. We applied the independent component analysis (ICA) to ventilator vibration signal analysis, used FastIC...

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Bibliographic Details
Published in2009 2nd International Congress on Image and Signal Processing pp. 1 - 4
Main Authors Yin Hong-sheng, Zhang Pei, Qian Jian-sheng, Hua Gang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2009
Subjects
Approximation algorithms
Entropy
Fault diagnosis
Feature extraction
Fourier transforms
Independent component analysis
Signal analysis
Signal processing
Signal processing algorithms
Support vector machines
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Summary:Ventilator vibration signal is usually mixed with some signals and shows strong nonlinearity, nonstationarity and nonGaussian. It presents a great challenge to feature extraction and recognition. We applied the independent component analysis (ICA) to ventilator vibration signal analysis, used FastICA algorithm to get a group of independent variables with the useful feature information, adopted residual self-information (RSI) to compress further for the group of independent variables, and chose the larger RSI to form the new estimating component. And then we used support vector machine (SVM) to find the ventilator healthy pattern and/or the ventilator fault pattern. The experiment result shows that by using the methods above the correct identification rate of ventilator healthy and fault state reaches 100%.
ISBN:1424441293
9781424441297
DOI:10.1109/CISP.2009.5304348