Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals

Mechanical Systems and Signal Processing

Volumn 76-77, Issue , 2016, Pages 283-293

  Li, Chuan ^a,b   Sanchez, René Vinicio ^b   Zurita, Grover ^b   Cerrada, Mariela ^b   Cabrera, Diego ^b   Vásquez, Rafael E ^c  

^a CHONGQING TECHNOLOGY AND BUSINESS UNIVERSITY   (China)

^b UNIVERSIDAD POLITÉCNICA SALESIANA   (Ecuador)

^c UNIVERSIDAD PONTIFICIA BOLIVARIANA   (Colombia)

Author keywords

Acoustic emission; Data fusion; Deep learning; Gearbox; Vibration signal

Indexed keywords

ACOUSTIC EMISSION TESTING; ACOUSTIC EMISSIONS; COMPUTER AIDED DIAGNOSIS; DATA FUSION; DATABASE SYSTEMS; DECISION TREES; FAILURE ANALYSIS; GEARS; PACKET NETWORKS;

ACOUSTIC EMISSION SENSORS; DEEP BOLTZMANN MACHINES; DEEP LEARNING; GEARBOX; OPERATIONAL CONDITIONS; STATISTICAL PARAMETERS; VIBRATION SIGNAL; WAVELET PACKET TRANSFORM(WPT);

FAULT DETECTION;

EID: 84963864627     PISSN: 08883270     EISSN: 10961216     Source Type: Journal    
DOI: 10.1016/j.ymssp.2016.02.007     Document Type: Article

References (39)

1. D. Galar, A. Thaduri, M. Catelani, and L. Ciani Context awareness for maintenance decision making: a diagnosis and prognosis approach Measurement 67 2015 137 150
2. Y. Lei, N. Li, and J. Lin Two new features for condition monitoring and fault diagnosis of planetary gearboxes J. Vib. Control 21 2015 755 764
3. J.J. Hou, W.K. Jiang, and W.B. Lu Application of a near-field acoustic holography-based diagnosis technique in gearbox fault diagnosis J. Vib. Control 19 2013 3 13
4. A.M.D. Younus, and B.S. Yang Intelligent fault diagnosis of rotating machinery using infrared thermal image Expert Syst. Appl. 39 2012 2082 2091
5. J.R. Ottewill, and M. Orkisz Condition monitoring of gearboxes using synchronously averaged electric motor signals Mech. Syst. Signal Process. 38 2013 482 498
6. C. Li, and M. Liang Extraction of oil debris signature using integral enhanced empirical mode decomposition and correlated reconstruction Meas. Sci. Technol. 22 2011 085701
7. J. Cheng, K. Zhang, and Y. Yang An order tracking technique for the gear fault diagnosis using local mean decomposition method Mech. Mach. Theory 55 2012 67 76
8. D. Wang, Q. Miao, and R. Kang Robust health evaluation of gearbox subject to tooth failure with wavelet decomposition J. Sound Vib. 324 2009 1141 1157
9. Y. Lei, D. Kong, J. Lin, and M.J. Zuo Fault detection of planetary gearboxes using new diagnostic parameters Meas. Sci. Technol. 23 2012 055605
10. B. Li, P.L. Zhang, Q. Mao, S.S. Mi, and P.Y. Liu Gear fault detection using adaptive morphological gradient lifting wavelet J. Vib. Control 19 2013 1646 1657
11. J.L.F. Chacon, E.A. Andicoberry, V. Kappatos, G. Asfis, T.H. Gan, W. Balachandran, Shaft angular misalignment detection using acoustic emission, Applied Acoustics, 85, pp. 12-22.
12. M. Hamel, A. Addali, and D. Mba Investigation of the influence of oil film thickness on helical gear defect detection using Acoustic Emission Appl. Acoust. 79 2014 42 46
13. M.R.A.A. Abad, H. Ahmadi, A. Moosavian, M. Khazaee, M.R. Kohan, and M. Mohammadi Discrete wavelet transform and artificial neural network for gearbox fault detection based on acoustic signals J. Vibroeng. 15 2013 459 463
14. S. Rafael, A. Romero, S. Soua, T.H. Gan, J. Neasham, and G. Goodfellow Detection of gearbox failures by combined acoustic emission and vibration sensing in rotating machinery Insight 56 2014 422 425
15. R.Y. Li, D. He, E. Bechhoefer, Investigation on fault detection for split torque gearbox using acoustic emission and vibration signals, in: Annual Conference of the Prognostics and Health Management Society, San Diego, CA, USA, 2009, pp. 1-11.
16. S. Soua, P.V. Lieshout, A. Perera, T.H. Gan, and B. Bridge Determination of the combined vibrational and acoustic emission signature of a wind turbine gearbox and generator shaft in service as a pre-requisite for effective condition monitoring Renew. Energy 51 2013 175 181
17. M. Khazaee, H. Ahmadi, M. Omid, A. Moosavian, and M. Khazaee Classifier fusion of vibration and acoustic signals for fault diagnosis and classification of planetary gears based on Dempster-Shafer evidence theory Proc. Inst. Mech. Eng. E - J. Process Mech. Eng. 228 2014 21 32
18. C. Li, and M. Liang Continuous-scale mathematical morphology-based optimal scale band demodulation of impulsive feature for bearing defect diagnosis J. Sound Vib. 331 2012 5864 5879
19. A. Raad, J. Antoni, and M. Sidahmed Indicators of cyclostationarity: theory and application to gear fault monitoring Mech. Syst. Signal Process. 22 2008 574 587
20. C. Li, M. Liang, and T. Wang Criterion fusion for spectral segmentation and its application to optimal demodulation of bearing vibration signals Mech. Syst. Signal Process. 64-65 2015 132 148
21. F. Chen, B. Tang, and R. Chen A novel fault diagnosis model for gearbox based on wavelet support vector machine with immune genetic algorithm Measurement 46 2013 220 232
22. C. Li, and M. Liang Time-frequency signal analysis for gearbox fault diagnosis using a generalized synchrosqueezing transform Mech. Syst. Signal Process. 26 1 2012 205 217
23. C. Li, V. Sanchez, G. Zurita, C.M. Lozada, and D. Cabrera Rolling element bearing defect detection using the generalized synchrosqueezing transform guided by time-frequency ridge enhancement ISA Trans. 60 2016 274 284
24. G.E. Hinton, and R.R. Salakhutdinov Reducing the dimensionality of data with neural networks Science 313 2006 504 507
25. K. Katayama, M. Ando, and T. horiguchi Model of MT and MST areas using an autoencoder Phys. A: Stat. Mech. Appl. 322 2013 531 545
26. T.N. Sainath, B. Kingsbury, G. Saon, H. Soltau, A. Mohamed, G. Dahl, and B. Ramabhadran Deep convolutional neural networks for multi-modality isointense infant brain image segmentation NeuroImage 108 2015 214 224
27. Y. Bai, Z. Chen, J. Xie, and C. Li Daily reservoir inflow forecasting using multiscale deep feature learning with hybrid models J. Hydrol. 532 2016 193 206
28. V.T. Tran, F.A. Thobiani, and A. Ball An approach to fault diagnosis of reciprocating compressor valves using Teager-Kaiser energy operator and deep belief networks Expert Syst. Appl. 41 2014 4113 4122
29. Z. Chen, C. Li, and R.V. Sanchez Multi-layer neural network with deep belief network for gearbox fault diagnosis J. Vibroeng. 17 5 2015 2379 2392
30. P. Tamilselvan, and P. Wang Failure diagnosis using deep belief learning based health state classification Reliab. Eng. Syst. Saf. 115 2013 124 135
31. R. Yan, R.X. Gao, and X. Chen Wavelets for fault diagnosis of rotary machines: a review with applications Signal Process. 96 2014 1 15
32. C. Li, R.V. Sanchez, G. Zurita, M. Cerrada, D. Cabrera, and R.E. Vásquez Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis Neurocomputing 168 2015 119 127
33. J. Schmidhuber Deep learning in neural networks: an overview Neural Netw. 61 2015 85 117
34. L. Shao, D. Wu, and X. Li Learning deep and wide: a spectral method for learning deep networks IEEE Trans. Neural Netw. Learn. Syst. 25 2014 2303 2308
35. D.J. Bordoloi, and R. Tiwari Optimum multi-fault classification of gears with integration of evolutionary and SVM algorithms Mech. Mach. Theory 73 2014 49 60
36. K. Cho, A. Ilin, and T. Raiko Improved learning of Gaussian-Bernoulli restricted Boltzmann machines Lect. Notes Comput. Sci. 6791 2011 10 17
37. Y. Tang, Deep learning using linear support vector machines, in: Workshop on Representational Learning, ICML 2013, Atlanta, USA, 2013.
38. L. Breiman Random forests Mach. Learn. 45 2001 5 32
39. R. Salakhutdinov, and G. Hinton An efficient learning procedure for deep Boltzmann machines Neural Comput. 24 2012 1967 2006