Bearing fault prognostics using Rényi entropy based features and Gaussian process models

Mechanical Systems and Signal Processing

Volumn 52-53, Issue 1, 2015, Pages 327-337

  Boškoski, Pavle ^a   Gašperin, Matej ^a,b   Petelin, Dejan ^a   Juričić, Dani ^a  

^a JO EF STEFAN INSTITUTE   (Slovenia)

^b UNIVERSITY OF WEST BOHEMIA   (Czech Republic)

Author keywords

Gaussian process models; Jensen R nyi divergence; Prognostics; Remaining useful life; R nyi entropy; Wavelet packet transform

Indexed keywords

CONDITION MONITORING; ENTROPY; GAUSSIAN NOISE (ELECTRONIC); MACHINERY; SYSTEMS ENGINEERING; PACKET NETWORKS;

DATA-DRIVEN APPROACH; GAUSSIAN PROCESS; GAUSSIAN PROCESS MODELS; LIKELIHOOD DISTRIBUTION; POSTERIOR DISTRIBUTIONS; PROGNOSTICS; REMAINING USEFUL LIVES; WAVELET PACKET TRANSFORMS; OPERATIONAL LIFE;

GAUSSIAN DISTRIBUTION;

EID: 84923061820     PISSN: 08883270     EISSN: 10961216     Source Type: Journal    
DOI: 10.1016/j.ymssp.2014.07.011     Document Type: Article

References (34)

1. P.F. Albrecht, J.C. Appiarius, D.K. Shrama, Assessment of the reliability of motors in utility applications, IEEE Trans. Energy Convers. EC-1 (1986) 39-46.
2. C.J. Crabtree, Survey of Commercially Available Condition Monitoring Systems for Wind Turbines, Technical Report, Durham University, School of Engineering and Computing Science, 2010.
3. N. Tandon, A. Choudhury, A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings, Tribol. Int. 32 (1999) 469-480.
4. F. Camci, K. Medjaher, N. Zerhouni, P. Nectoux, Feature Evaluation for Effective Bearing Prognostics, Quality and Reliability Engineering International, ISSN 1099-1638.
5. Y. Li, T. Kurfess, S. Liang, Stochastic prognostics for rolling element bearings, Mech. Syst. Signal Process. 14 (5) (2000) 747-762.
6. N. Lybeck, S. Marble, B. Morton, Validating prognostic algorithms: a case study using comprehensive bearing fault data, in: Aerospace Conference, IEEE, 2007, pp. 1-9.
7. M.N. Kotzalas, T.A. Harris, Fatigue failure progression in ball bearings, Trans. ASME 123 (2001) 238-242.
8. R. Li, P. Sopon, D. He, Fault features extraction for bearing prognostics, J. Intell. Manuf. 23 (2012) 313-321. ISSN 0956-5515.
9. P. Borghesani, P. Pennacchi, S. Chatterton, The relationship between kurtosis- and envelope-based indexes for the diagnostic of rolling element bearings, Mech. Syst. Signal Process. 43 (1-2) (2014) 25-43. ISSN 0888-3270.
10. J. Qiu, B.B. Seth, S.Y. Liang, C. Zhang, Damage mechanics approach for bearing lifetime prognostics, Mech. Syst. Signal Process. 16 (5) (2002) 817-829.
11. Y. Pan, J. Chen, L Guo, Robust bearing performance degradation assessment method based on improved wavelet packet-support vector data description, Mech. Syst. Signal Process. 23 (3) (2009) 669-681. ISSN 0888-3270.
12. D. Wcng, Q. Miao, R. Kang, Robust health evaluation of gearbox subject to tooth failure with wavelet decomposition, J. Sound Vib. 324 (3-5) (2009) 1141-1157.
13. H. Ocak, K.A. Loparo, F.M. Discenzo, Online tracking of bearing wear using wavelet packet decomposition and probabilistic modeling: a method for bearing prognostics, J. Sound Vib. 302 (4-5) (2007) 951-961.
14. S. Janjarasjitt, H. Ocak, K. Loparo, Bearing condition diagnosis and prognosis using applied nonlinear dynamical analysis of machine vibration signal, J. Sound Vib. 317 (1-2) (2008) 112-126.
15. P. Boškoski, D.J. Juričić, Fault detection of mechanical drives under variable operating conditions based on wavelet packet Rényi entropy signatures, Mech. Syst Signal Process. 31 (2012) 369-381. ISSN 0888-3270.
16. P. Boškoski, D.J. Juričić, Rényi entropy based statistical complexity analysis for gear fault prognostics under variable load, in: T. Fakhfakh, W. Bartelmus, F. Chaari, R. Zimroz, M. Haddar (Eds.), Condition Monitoring of Machinery in Non-Stationary Operations, Springer, Berlin, Heidelberg, 2012, pp. 25-32. ISBN 978-3-642-28767-1.
17. K. Ažman, J. Kocijan, Application of Gaussian processes for black-box modelling of biosystems, ISA Trans. 46 (4) (2007) 443-457.
18. Z. Južnič-Zonta, J. Kocijan, X. Flotats, D. Vrečko, Multi-criteria analyses of wastewater treatment bio-processes under an uncertainty and a multiplicity of steady states, Water Res. 46 (18) (2012) 6121-6131.
19. B. Grašič, P. Mlakar, M.Z. Božnar, Ozone prediction based on neural networks and Gaussian processes, Nuovo Cim. C 29 (2006) 651-661.
20. B. Likar, J. Kocijan, Predictive control of a gas-liquid separation plant based on a Gaussian process model, Comput. Chem. Eng. 31 (3) (2007) 142-152.
21. J. Kocijan, V. Tanko, Prognosis of gear health using Gaussian process model, in: Proceedings of EUROCON 2011, International Conference on Computer as a Tool, Lisbon, Portugal, 2011, pp. 1-4.
22. P. Nectoux, P. Gouriveau, K. Medjaher, E. Ramasso, B. Morello, N. Zerhouni, C. Varnier, PRONOSTIA: an experimental platform for bearings accelerated life test, in: IEEE International Conference on Prognostics and Health Management, Denver, CO, USA, 2012.
23. R.B. Randall, J. Antoni, S. Chobsaard, The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals, Mech. Syst. Signal Process. 15 (2001) 945-962.
24. J. Antoni, Cyclostationarity by examples, Mech. Syst. Signal Process. 23 (2009) 987-1036.
25. S. Mallat, A Wavelet Tour of Signal Processing, 3rd ed. Academic Press, Burlington, MA, 2008.
26. D.B. Percival, A.T. Walden, Wavelet Methods for Time Series Analysis, Cambridge University Press, Cambridge, 2000.
27. S. Blanco, A. Figliola, R.Q. Quiroga, O.A. Rosso, E. Serrano, Time-frequency analysis of electroencephalogram series. III. Wavelet packets and information cost function, Phys. Rev. E 57 (1) (1998) 932-940.
28. A. Rényi, On measures of information and entropy, in: 4th Berkeley Symposium on Mathematics, Statistics and Probability, 1960.
29. D. Xu, D. Erdogmuns, Rényi's entropy, divergence and their nonparametric estimators, in: Information Theoretic Learning, Springer, New York, 2010, pp. 47-102.
30. M. Basseville, Divergence Measures for Statistical Data Processing, Technical Report, IRISA, 2010.
31. A.O. Hero, B. Ma, O. Michel, J. Gorman, Alpha-Divergence for Classification, Indexing and Retrieval, Technical Report CSPL-328, Communications and Signal Processing Laboratory, The University of Michigan, 2002.
32. X.C.R. López-Ruiz, H.L. Mancini b, A statistical measure of complexity, Phys. Lett. A 209 (1995) 321-326.
33. D.J.C. MacKay, Introduction to Gaussian processes, NATO ASI Series 168 (1998) 133-166.
34. CE. Rasmussen, C.K.I. Williams, Gaussian Processes for Machine Learning, The MIT Press, Cambridge, MA, USA, 2006.