Full Text:   <1611>

CLC number: TH17; TP18

On-line Access: 2010-03-29

Received: 2009-06-19

Revision Accepted: 2010-01-06

Crosschecked: 2010-03-10

Cited: 24

Clicked: 4596

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
1. Reference List
Open peer comments

Journal of Zhejiang University SCIENCE A 2010 Vol.11 No.4 P.270-279

http://doi.org/10.1631/jzus.A0900360


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


Author(s):  Guo-liang Xiong, Long Zhang, He-sheng Liu, Hui-jun Zou, Wei-zhong Guo

Affiliation(s):  School of Mechatronic Engineering, East China Jiaotong University, Nanchang 330013, China, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China, Department of Physics, Shangrao Normal University, Shangrao 334001, China

Corresponding email(s):   longzh@126.com, lgxcxx@ecjtu.jx.cn

Key Words:  Fault diagnosis, Bearing, Multiscale entropy, Feature extraction, Support vector machines (SVMs)


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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Abbasion, S., Rafsanjani, A., Farshidianfar, A., Irani, N., 2007. Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine. Mechanical Systems and Signal Processing, 21(7): 2933-2945.

[2]Chen, W., Wang, Z., Ren, X., 2006. Characterization of surface EMG signals using improved approximate entropy. Journal of Zhejiang University-SCIENCE B, 7(10):844-848.

[3]Chen, W., Zhuang, J., Yu, W., Wang, Z., 2009. Measuring complexity using FuzzyEn, ApEn, and SampEn. Medical Engineering & Physics, 31(1):61-68.

[4]Costa, M., Goldberger, A.L., Peng, C.K., 2002. Multiscale entropy analysis of complex physiologic time series. Physical Review Letters, 89(6):068102.

[5]Costa, M., Goldberger, A.L., Peng, C.K., 2005. Multiscale entropy analysis of biological signals. Physical Review E, 71(2):021906.

[6]Escudero, J., Abasolo, D., Hornero, R., Espino, P., Lopez, M., 2006. Analysis of electroencephalograms in Alzheimer’s disease patients with multiscale entropy. Physiological Measurement, 27(11):1091-1106.

[7]Fan, X.F., Liang, M., Yeap, T.H., Kind, B., 2007. A joint wavelet lifting and independent component analysis approach to fault detection of rolling element bearings. Smart Materials & Structures, 16(5):1973-1987.

[8]Hsu, C., Chang, C., Lin, C., 2009. A Practical Guide to Support Vector Classification. Available from http://www. csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf [Accessed on May 20, 2009].

[9]Hu, Z., Cai, Y., Li, Y., Xu, X., 2005. Data fusion for fault diagnosis using multi-class Support Vector Machines. Journal of Zhejiang University-SCIENCE A, 6(10):1030-1039.

[10]Jiang, J.D., Chen, J., Qu, L.S., 1999. The application of correlation dimension in gearbox condition monitoring. Journal of Sound and Vibration, 223(4):529-541.

[11]Jiang, Z., Fu, H.G., Li, L.J., 2005. Support Vector Machine for mechanical faults classification. Journal of Zhejiang University-SCIENCE A, 6(5):433-439.

[12]Kaffashi, F., Foglyano, R., Wilson, C.G., Loparo, K.A., 2008. The effect of time delay on Approximate & Sample Entropy calculations. Physica D Nonlinear Phenomena, 237(23):3069-3074.

[13]Li, Z.W., Zhang, Y.K., 2008. Multi-scale entropy analysis of Mississippi river flow. Stochastic Environmental Research and Risk Assessment, 22(4):507-512.

[14]Li, Z.Y., Wu, W.L., 2008. Classification of power quality combined disturbances based on phase space reconstruction and support vector machines. Journal of Zhejiang University-SCIENCE A, 9(2):173-181.

[15]Liao, F.Y., Wang, J., He, P., 2008. Multi-resolution entropy analysis of gait symmetry in neurological degenerative diseases and amyotrophic lateral sclerosis. Medical Engineering & Physics, 30(3):299-310.

[16]Logan, D., Mathew, J., 1996. Using the correlation dimension for vibration fault diagnosis of rolling element bearing-I. basic concepts. Mechanical Systems and Signal Processing, 10(3):241-250.

[17]Loparo, K.A., 2005. Bearing Vibration Data: Case Western Reserve University Bearing Data Center Website. Available from http://www.eecs.case.edu/laboratory/ bearing/welcome_overview.htm [Accessed on June 23, 2008].

[18]Lou, X.S., Loparo, K.A., 2004. Bearing fault diagnosis based on wavelet transform and fuzzy inference. Mechanical Systems and Signal Processing, 18(5):1077-1095.

[19]Nguyen, N.T., Lee, H.H., Kwon, J.M., 2008. Optimal feature selection using genetic algorithm for mechanical fault detection of induction motor. Journal of Mechanical Science and Technology, 22(3):490-496.

[20]Pincus, S.M., 1991. Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences USA, 88(6):2297-2301.

[21]Richman, J.S., Moorman, J.R., 2000. Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology-Heart and Circulatory Physiology, 278(6):H2039-H2049.

[22]Wang, J., Ma, Q.L., 2008. Multiscale entropy based study of the pathological time series. Chinese Physics B, 17(12):4424-4427.

[23]Wang, W.J., Chen, J., Wu, X.K., Wu, Z.T., 2001. The application of some non-linear methods in rotating machinery fault diagnosis. Mechanical Systems and Signal Processing, 15(4):697-705.

[24]Widodo, A., Yang, B.S., 2008. Wavelet support vector machine for induction machine fault diagnosis based on transient current signal. Expert Systems with Applications, 35(3):307-316.

[25]Xu, Y.G., Li, L.J., He, Z.J., 2002. Approximate entropy and its applications in mechanical fault diagnosis. Information and Control, 31(6):547-551 (in Chinese).

[26]Yan, R.Q., Gao, R.X., 2007. Approximate Entropy as a diagnostic tool for machine health monitoring. Mechanical Systems and Signal Processing, 21(2):824-839.

[27]Yan, Z.G., Wang, Z.Z., Ren, X.M., 2007. Joint application of feature extraction based on EMD-AR strategy and multi-class classifier based on LS-SVM in EMG motion classification. Journal of Zhejiang University-SCIENCE A, 8 (8):1246-1255.

[28]Yuan, S.F., Chu, F.L., 2006. Support vector machines-based fault diagnosis for turbo-pump rotor. Mechanical Systems and Signal Processing, 20(4):939-952.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - Journal of Zhejiang University-SCIENCE