Health condition identification of multi-stage planetary gearboxes using a mRVM-based method

Mechanical Systems and Signal Processing

Volumn 60, Issue , 2015, Pages 289-300

  Lei, Yaguo ^a   Liu, Zongyao ^a   Wu, Xionghui ^a   Li, Naipeng ^a   Chen, Wu ^a   Lin, Jing ^a  

^a XI'AN JIAOTONG UNIVERSITY   (China)

Author keywords

Fault classification; Health condition identification; Multi stage planetary gearboxes; Relevance vector machine

Indexed keywords

AUTOMOTIVE INDUSTRY; HEALTH;

FAULT CLASSIFICATION; HEALTH CONDITION; HEAVY INDUSTRIES; LOADING CONDITION; MULTI-CLASS RELEVANCE VECTOR MACHINE; PLANETARY GEARBOXES; RELEVANCE VECTOR MACHINE; WORKING ENVIRONMENT;

EPICYCLIC GEARS;

EID: 84925969865     PISSN: 08883270     EISSN: 10961216     Source Type: Journal    
DOI: 10.1016/j.ymssp.2015.01.014     Document Type: Article

References (38)

1. X.F. Fan, M.J. Zuo, Condition monitoring of low speed planetary gearboxes, Technical Report, Department of Mechanical Engineering, University of Alberta, February 9, 2006, 32 pp.
2. Y.G. Lei, J. Lin, and M.J. Zuo Condition monitoring and fault diagnosis of planetary gearboxes: a review Measurement 48 2 2014 292 305
3. Y.C. Guo, and R.G. Parker Analytical determination of mesh phase relations in general compound planetary gears Mech. Mach. Theory 46 2011 1869 1887
4. F. Chaari, T. Fakhfakh, and M. Haddar Dynamic analysis of a planetary gear failure caused by tooth pitting and cracking J. Fail. Anal. Prev. 6 2006 73 78
5. Z. Cheng, N.Q. Hu, and X.F. Zhang Crack level estimation approach for planetary gearbox based on simulation signal and GRA J. Sound Vib. 331 2012 5853 5863
6. Z.P. Feng, and M.J. Zuo Fault diagnosis of planetary gearboxes via torsional vibration signal analysis Mech. Syst. Signal Process. 36 2013 401 421
7. Z.P. Feng, and M.J. Zuo Vibration signal models for fault diagnosis of planetary gearboxes J. Vib. Acoust. 331 2012 4919 4939
8. W. Bartelmus, and R. Zimroz Vibration condition monitoring of planetary gearbox under varying external load Mech. Syst. Signal Process. 23 2009 246 257
9. Z. Cheng, N.Q. Hu, and F.S. Gu Pitting damage levels estimation for planetary gear sets based on model simulation and grey relational analysis Trans. Can. Soc. Mech. Eng. 35 2011 403 417
10. D.M. Blunt, and J.A. Keller Detection of a fatigue crack in a UH-60A planet gear carrier using vibration analysis Mech. Syst. Signal Process. 20 2006 2095 2111
11. W. Bartelmus, and R. Zimroz A new feature for monitoring the condition of gearboxes in non-stationary operating conditions Mech. Syst. Signal Process. 23 2009 1528 1534
12. Y.G. Lei, D.T. Kong, and J. Lin Fault detection of planetary gearboxes using new diagnostic parameters Meas. Sci. Technol. 23 2012 1 10
13. B. Zhang, T. Khawaja, and R. Patrick A novel blind deconvolution de-noising scheme in failure prognosis Trans. Inst. Meas. Control 32 1 2010 3 30
14. P.D. Samuel, and D.J. Pines Constrained adaptive lifting and the CAL4 metric for helicopter transmission diagnostics J. Sound Vib. 319 2 2009 698 718
15. Y.G. Lei, D. Han, and J. Lin Planetary gearbox fault diagnosis using an adaptive stochastic resonance method Mech. Syst. Signal Process. 38 1 2013 113 124
16. Z.P. Feng, M. Liang, and Y. Zhang Fault diagnosis for wind turbine planetary gearboxes via demodulation analysis based on ensemble empirical mode decomposition and energy separation Renew. Energy 47 2012 112 126
17. J. Antoni The spectral kurtosis: a useful tool for characterizing non-stationary signals Mech. Syst. Signal Process. 20 2006 282 307
18. T. Barszcz, and R.B. Randall Application of spectral kurtosis for detection of a tooth crack in the planetary gear of a wind turbine Mech. Syst. Signal Process. 23 2009 1352 1365
19. M.E. Tipping The relevance vector machine Adv. Neural Inf. Process. Syst. 12 2000 652 658
20. M.E. Tipping Sparse Bayesian learning and the relevance vector machine J. Mach. Learn. Res. 1 2001 211 244
21. A. Bharathi, and A.M. Natarajan Cancer classification using support vector machine and relevance vector machine based on analysis of variance features J. Comput. Sci. 7 9 2011 1393 1399
22. C.A.M. Lima, A.L.V. Coelho, and S. Chagas Automatic EEG signal classification for epilepsy diagnosis with relevance vector machines Expert Syst. Appl. 36 2009 10054 10059
23. C.L. Phillips, M.A. Bruno, and P. Maquet "Relevance vector machine" consciousness classifier applied to cerebral metabolism of vegetative and locked-in patients NeuroImage 56 2011 797 808
24. B. Demir, and S. Ertürk Hyperspectral image classification using relevance vector machines IEEE Geosci. Remote Sens. Lett. 4 4 2007 586 590
25. A. Tolambiya, S. Venkatraman, and P.K. Kalra Content-based image classification with wavelet relevance vector machines Soft Comput. 14 2010 129 136
26. C. Silva, and B. Ribeiro Scaling text classification with relevance vector machines IEEE Int. Conf. Syst. Man Cybern. 5 2006 2006 4186 4191
27. X.D. Wang, M.Y. Ye, and C.J. Duanmu Classification of data from electronic nose using relevance vector machines Sens. Actuators B 140 2009 143 148
28. G.M. Foody RVM-based multi-class classification of remotely sensed data Int. J. Remote Sens. 29 6 2008 1817 1823
29. A. Widodo, E.Y. Kim, and J.D. Son Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine Expert Syst. Appl. 36 2009 7252 7261
30. W. Caesarendra, A. Widodo, and B.S. Yang Application of relevance vector machine and logistic regression for machine degradation assessment Mech. Syst. Signal Process. 24 2010 1161 1171
31. A. Widodo, and B.S. Yang Application of relevance vector machine and survival probability to machine degradation assessment Expert Syst. Appl. 38 2011 2592 2599
32. G.F. Wang, Y.W. Yang, and Q.L. Xie Force based tool wear monitoring system for milling process based on relevance vector machine Adv. Eng. Softw. 71 2014 46 51
33. I. Psorakis, T. Damoulas, and M.A. Girolami Multiclass relevance vector machines: sparsity and accuracy IEEE Trans. Neural Netw. 21 10 2010 1588 1598
34. M. Inalpolat, and A. Kahraman A theoretical and experimental investigation of modulation sidebands of planetary gear sets J. Sound Vib. 323 2009 677 696
35. W.D. Mark, and J.A. Hines Stationary transducer response to planetary-gear vibration excitation with non-uniform planet loading Mech. Syst. Signal Process. 23 4 2009 1366 1381
36. J.S.R. Jang ANFIS: adaptive-network-based fuzzy inference system IEEE Trans. Syst. Man Cybern. 23 1993 665 685
37. L. Zhang, G. Xiong, and H. Liu Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference Expert Syst. Appl. 37 2010 6077 6085
38. A. Soualhi, H. Razik, and G. Clerc Prognosis of bearing failures using hidden Markov models and the adaptive Neuro-Fuzzy inference system IEEE Trans. Ind. Electron. 61 2014 2864 2874