CLC number: TH17; TP18
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2010-03-10
Cited: 24
Clicked: 7496
Guo-liang Xiong, Long Zhang, He-sheng Liu, Hui-jun Zou, Wei-zhong Guo. A comparative study on ApEn, SampEn and their fuzzy counterparts in a multiscale framework for feature extraction[J]. Journal of Zhejiang University Science A, 2010, 11(4): 270-279.
@article{title="A comparative study on ApEn, SampEn and their fuzzy counterparts in a multiscale framework for feature extraction",
author="Guo-liang Xiong, Long Zhang, He-sheng Liu, Hui-jun Zou, Wei-zhong Guo",
journal="Journal of Zhejiang University Science A",
volume="11",
number="4",
pages="270-279",
year="2010",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A0900360"
}
%0 Journal Article
%T A comparative study on ApEn, SampEn and their fuzzy counterparts in a multiscale framework for feature extraction
%A Guo-liang Xiong
%A Long Zhang
%A He-sheng Liu
%A Hui-jun Zou
%A Wei-zhong Guo
%J Journal of Zhejiang University SCIENCE A
%V 11
%N 4
%P 270-279
%@ 1673-565X
%D 2010
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A0900360
TY - JOUR
T1 - A comparative study on ApEn, SampEn and their fuzzy counterparts in a multiscale framework for feature extraction
A1 - Guo-liang Xiong
A1 - Long Zhang
A1 - He-sheng Liu
A1 - Hui-jun Zou
A1 - Wei-zhong Guo
J0 - Journal of Zhejiang University Science A
VL - 11
IS - 4
SP - 270
EP - 279
%@ 1673-565X
Y1 - 2010
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A0900360
Abstract: feature extraction from vibration signals has been investigated extensively over the past decades as a key issue in machine condition monitoring and fault diagnosis. Most existing methods, however, assume a linear model of the underlying dynamics. In this study, the feasibility of devoting nonlinear dynamic parameters to characterizing bearing vibrations is studied. Firstly, fuzzy sample entropy (FSampEn) is formulated by defining a fuzzy membership function with clear physical meaning. Secondly, inspired by the multiscale sample entropy (multiscale SampEn) which is originally proposed to quantify the complexity of physiological time series, we placed approximate entropy (ApEn), fuzzy approximate entropy (FApEn) and the proposed FSampEn into the same multiscale framework. This led to the developments of multiscale ApEn, multiscale FApEn and multiscale FSampEn. Finally, all four multiscale entropies along with their single-scale counterparts were employed to extract discriminating features from bearing vibration signals, and their classification performance was evaluated using support vector machines (SVMs). Experimental results demonstrated that all four multiscale entropies outperformed single-scale ones, whilst multiscale FSampEn was superior to other multiscale methods, especially when analyzed signals were contaminated by heavy noise. Comparisons with statistical features in time domain also support the use of multiscale FSampEn.
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