Jixie qiangdu (Jan 2016)
FAULT FEATURE EXTRATION METHOD BASED ON LCD FUZZY ENTROPY AND MANIFOLD LEARNING
Abstract
Aiming at the fact that the vibration signal of hydraulic pump would exactly display non-stationary characteristics and fault features hard to extracted,a feature extraction method of hydraulic pump based on LCD（ local characteristic-scale decomposition） fuzzy entropy and manifold learning was proposed. The proposed method combined the LCD,fuzzy entropy and manifold learning. Firstly,the vibration signals was decomposed into several ISCs（ intrinsic scale component） and fuzzy entropy of each ISC was calculated,and the high-dimension fault feature was preliminarily extracted. Secondly,LLTSA（ liner local tangent space alignment） which is one of typical manifold learning methods was applied to compress the high-dimension features into low-dimension features which have better discrimination. Finally,the SVM（ support vector machine） was employed to evaluate the feature extraction method. Experiment results of hydraulic pump show that the proposed method can classify different fault type of hydraulic pump exactly and has certain superiority.
