A comparative study on ApEn,SampEn and their fuzzy counterparts in a multiscale framework for feature extraction  

作　　者：Guo-liang XIONG Long ZHANG He-sheng LIU Hui-jun ZOU Wei-zhong GUO 

机构地区：[1]School of Mechatronie Engineering, East China Jiaotong University, Nanchang 330013, China [2]School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China [3]Department of Physics, Shangrao Normal University, Shangrao 334001, China

出　　处：《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》2010年第4期270-279,共10页浙江大学学报（英文版）A辑（应用物理与工程）

基　　金：supported by the National Natural Science Foundation of China (Nos.50875161 and 50821003);the Natural Science Foundation of Jiangxi Province,China (No.0450017)

摘　　要：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.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 ofmultiscale FSampEn.

关 键 词：Fault diagnosis BEARING Multiscale entropy Feature extraction Support vector machines (SVMs) 

分 类 号：TH17[机械工程—机械制造及自动化] TP18[自动化与计算机技术—控制理论与控制工程]