Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis

ISA Transactions

Volumn 53, Issue 5, 2014, Pages 1650-1660

  Saidi, Lotfi ^a   Ali, Jaouher Ben ^a   Fnaiech, Farhat ^a  

^a UNIVERSITY OF TUNIS   (Tunisia)

Author keywords

Bi spectrum; Empirical mode decomposition; Fault diagnosis; Induction motor; Intrinsic mode function; Rolling element bearing

Indexed keywords

FAILURE ANALYSIS; FUNCTIONS; GAUSSIAN NOISE (ELECTRONIC); INDUCTION MOTORS; ROLLER BEARINGS; SIGNAL DETECTION; SPECTRUM ANALYSIS; VIBRATION ANALYSIS; WHITE NOISE;

BEARING VIBRATIONS; BISPECTRUM; EMPIRICAL MODE DECOMPOSITION; INDUCTION MACHINES; INTRINSIC MODE FUNCTIONS; LOCAL CHARACTERISTICS; NON-STATIONARY VIBRATION SIGNALS; ROLLING ELEMENT BEARING;

FAULT DETECTION;

EID: 84908118070     PISSN: 00190578     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isatra.2014.06.002     Document Type: Conference Paper

References (26)

1. C. Seungdeog, B. Akin, M.M. Rahimian, and H.A. Toliyat Implementation of a fault-diagnosis algorithm for induction machines based on advanced digital-signal-processing techniques IEEE Trans Ind Electron 58 3 2011 937 948
2. L. Saidi, H. Henao, F. Fnaiech, G.-A. Capolino, and G. Cirrincione Diagnosis of broken bars fault in induction machines using higher order spectral analysis ISA Trans 52 2013 140 148
3. R.B. Randall, and J. Antoni Rolling element bearing diagnostics - a tutorial Mech Syst Signal Process 25 2 2011 485 520
4. Saidi L, Fnaiech F, Capolino G-A, Henao H. Stator current bi-spectrum patterns for induction machines multiple-faults detection. In: Proceedings of IEEE IECON; 2012. p. 5132-7.
5. F.E.H. Montero, and O.C. Medina The application of bispectrum on diagnosis of rolling element bearings: a theoretical approach Mech Syst Signal Process 22 2008 588 596
6. S. Guoji, S. McLaughlin, X. Yongcheng, and P. White Theoretical and experimental analysis of bispectrum of vibration signals for fault diagnosis of gears Mech Syst Signal Process 43 2014 76 89
7. S. Braun, and M. Feldman Decomposition of non-stationary signals into varying times scales: some aspects of the EMD and HVD methods Mech Syst Signal Process 25 2011 2608 2630
8. Y. Lei, J. Lin, Z. He, and M.J. Zuo A review on empirical mode decomposition in fault diagnosis of rotating machinery Mech Syst Signal Process 35 2013 108 126
9. Flandrin P, Goncalvès P, Rilling G. Detrending and denoising with empirical mode decomposition. In: Proceedings of the 12th European signal processing conference; 2004. p. 1581-84.
10. Mendel JM. Tutorial on higher order statistics (spectra) in signal processing and system theory: theoretical results and some applications. In: Proceedings of the IEEE, vol. 79; 1991. p. 287-05.
11. C.L. Nikias, and A. Petropulu Higher-order spectra analysis: a nonlinear signal processing framework 1993 Prentice-Hall Englewood Cliffs, New Jersey
12. Y. Zhou, J. Chen, G.M. Dong, W.B. Xiao, and Z.Y. Wang Application of the horizontal slice of cyclic bispectrum in rolling element bearings diagnosis Mech Syst Signal Process 26 2012 229 243
13. Saidi L., Henao H., Fnaiech F., Capolino G.-A., Cirrincione G. Application of higher order spectra analysis for rotor broken bar detection in induction machines. In: Proceedings of IEEE international symposium on diagnostics for electric machines power electronics & drives (SDEMPED); 2011. p. 31-8.
14. H. Li Order bi-spectrum for bearing fault monitoring and diagnosis under run-up condition J Comput 6 9 2011 1994 2000
15. L.J. Luo, Y. Yan, and P. Xie Hilbert-Huang transform, Hurst and chaotic analysis based flow regime identification methods for an airlift reactor Chem Eng J 181-182 2012 570 580
16. J. Tang, L.J. Zhao, and H. Yue Vibration analysis based on empirical mode decomposition and partial least square Proc Eng 16 2011 646 652
17. Lin JS, Chen Q. Application of the EEMD method to multiple faults diagnosis of gearbox. In: Proceedings of the second international conference on advanced computer control; 27-29, 2010. p. 395-9.
18. Miao Q, Wang D, Pecht M. Rolling element bearing fault feature extraction using EMD-based independent component analysis. In: Proceedings of IEEE conference on prognostics and health management; 2011. p. 1-6.
19. D.J. Yu, J.S. Cheng, and Y. Yang Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings Mech Syst Signal Process 19 2005 259 270
20. Li H, Zheng HQ. Bearing fault detection using envelope spectrum based on EMD and TKEO. In: Proceedings of the international conference on fuzzy systems and knowledge discovery; 2008. p. 142-6.
21. V.K. Rai, and A.R. Mohanty Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform Mech Syst Signal Process 21 2007 2607 2615
22. H. Li, H.Q. Zheng, and L.W. Tang Wigner-Ville distribution based on EMD for faults diagnosis of bearing Fuzzy Syst Knowl Discov 4223 2006 803 812
23. Chen F., Zhou X., Wu Q.H. Application of Hilbert-Huang transformation to fault diagnosis of rotary machinery. In: Proceedings of International Symposium on Instrumentation Science and Technology; 2008. p. 15-8.
24. Case Western Reserve University Bearing Data Center. Download a data file [DB/OL]. Available from: "http://www.eecs.cast.edu/laboratory/bearing/download.htm" (accessed 25.12.13).
25. M.B. Priestley Nonlinear and nonstationary time series analysis 1988 Academic Press NewYork
26. T. Fawcett An introduction to ROC analysis Pattern Recognit Lett 27 2006 861 874