An intelligent fault diagnosis method based on multiscale entropy and SVMs

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5553 LNCS, Issue PART 3, 2009, Pages 724-732

  Zhang, Long ^a   Xiong, Guoliang ^b   Liu, Hesheng ^c   Zou, Huijun ^a   Guo, Weizhong ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b EAST CHINA JIAOTONG UNIVERSITY   (China)

^c SHANGRAO NORMAL UNIVERSITY   (China)

Author keywords

Fault diagnosis; Multiscale entropy; Sample entropy; SVMs

Indexed keywords

ARTIFICIAL NEURAL NETWORKS; FAULT DIAGNOSIS; INTELLIGENT FAULT DIAGNOSIS; MECHANICAL SYSTEMS; MECHANICAL VIBRATION SIGNALS; MULTIPLE TIME SCALE; MULTISCALE ENTROPY; PARAMETER SELECTION; SAMPLE ENTROPY; STATISTICAL FEATURES; SVMS; VIBRATION SIGNAL;

ENTROPY; FAILURE ANALYSIS; FEATURE EXTRACTION; MEAN SQUARE ERROR; NEURAL NETWORKS; STRAIN ENERGY; TIME SERIES; VIBRATIONS (MECHANICAL);

SUPPORT VECTOR MACHINES;

EID: 69849084029     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-01513-7_79     Document Type: Conference Paper

References (19)

1. Yan, R.Q., Gao, R.X.: Approximate Entropy as a Diagnostic Tool for Machine Health Monitoring. Mechanical Systems and Signal Processing 21, 824-839 (2007)
2. Yan, R.Q., Gao, R.X.: Machine Health Diagnosis Based on Approximate Entropy. In: ICMT 2004 Instrumentation and Measurement Technology Conference, pp. 2054-2059. IEEE Press, Italy (2004)
3. Jiang, J.D., Chen, J.: The Application of Correlation Dimension in Gearbox Condition Monitoring. Journal of Sound and Vibration 224, 529-541 (2004)
4. Logan, D., Mathew, J.: Using the Correlation Dimension for Vibration Fault Diagnosis of Rolling Element Bearings-I. Basic Concepts. Mechanical Systems and Signal Processing 10(3), 241-250 (1996) (Pubitemid 126359392)
5. Logan, D., Mathew, J.: Using the Correlation Dimension for Vibration Fault Diagnosis of Rolling Element Bearings-II. Selection of Experimental Parameters. Mechanical Systems and Signal Processing 10(3), 251-260 (1996)
6. Xu, Y.G., Li, L.J., He, Z.J.: Approximate Entropy and Its Applications in Mechanical Fault Diagnosis. Information and Control 31(6), 547-551 (2002)
7. Richman, J.S., Moorman, J.R.: Physiological Time-Series Analysis Using Approximate Entropy and Sample Entropy. Am. J. Physiol. H. 278, 2039-2049 (2000)
8. Haitham, M.A., Alan, V.S.: Use of Sample Entropy Approach to Study Heart Rate Variability in Obstructive Sleep Apnea Syndrome. IEEE Trans. Bio. Eng. 50, 1900-1904 (2007)
9. Costa, M., Goldberger, A.L., Peng, C.K.: Multiscale Entropy Analysis of Complex Physiologic Time Series. Phys. Res. Lett. 89(6), 68-102 (2002)
10. Costa, M., Goldberger, A.L., Peng, C.K.: Multiscale Entropy to Distiguish Physiologic and Synthetic RR Time Series. Computers in Cardiology 29, 137-140 (2002)
11. Costa, M., Goldberger, A.L., Peng, C.K.: Multiscale Entropy Analysis of Biological Signals. Phys. Rev. E. 71, 21906 (2005)
12. Ranjit, A.T., Georg, A.G.: On Multiscale Entropy Analysis for Physiological Data. Physica A 366, 323-332 (2006)
13. Fan, X.F., Zuo, M.J.: Machine Fault Feature Extraction Based on Intrinsic Mode Functions. Meas. Sci. Technol. 19, 245105(12pp) (2008)
14. Lu, S., Yu, F.J., Liu, J.: Bearing Fault Diagnosis Based on K-L Transform and Support Vector Machine. In: 3rd IEEE International Conference on Natural Computation, pp. 522-527. IEEE Press, New York (2007)
15. Trunkvalterova, Z., Javorka, M., Tonhajzerova, I., Javorkova, J., Lazarova, Z., Javorka, K., Baumert, M.: Reduced Short-Term Complexity of Heart Rate and Blood Resssure Dynamics in Patients with Diabetes Mellitus Type 1: Multiscale Entropy Analysis. Physiol. Meas. 29, 817-828 (2008)
16. Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, Berlin (1995)
17. Case Western Reserve University Bearing Data Center Website, http://www.eecs.case.edu/laboratory/bearing/welcome-overview.htm
18. Lou, X.S., Loparo, K.A.: Bearing Fault Diagnosis Based on Wavelet Transform and Fuzzy Inference. Mechanical Systems and Signal Processing 18, 1077-1095 (2004)
19. Hsu, C.W., Chang, C.C., Lin, C.J.: A Practical Guide to Support Vector Classification (2008), http://www.csie.ntu.edu.tw/∼cjlin