Deep neural networks-based rolling bearing fault diagnosis

Microelectronics Reliability

Volumn 75, Issue , 2017, Pages 327-333

  Chen, Zhiqiang ^a   Deng, Shengcai ^a   Chen, Xudong ^a   Li, Chuan ^a   Sanchez, René Vinicio ^b   Qin, Huafeng ^a  

^a CHONGQING TECHNOLOGY AND BUSINESS UNIVERSITY   (China)

^b UNIVERSIDAD POLITÉCNICA SALESIANA   (Ecuador)

Author keywords

Deep Belief Networks; Deep Boltzmann Machines; Fault diagnosis; Rolling bearing; Stacked Auto Encoders

Indexed keywords

BEARINGS (MACHINE PARTS); DEEP NEURAL NETWORKS; FAILURE ANALYSIS; FREQUENCY DOMAIN ANALYSIS; LEARNING SYSTEMS; MACHINERY; ROLLER BEARINGS; SIGNAL ENCODING;

AUTO ENCODERS; DEEP BELIEF NETWORKS; DEEP BOLTZMANN MACHINES; FAULT DIAGNOSIS TECHNOLOGY; NEURAL NETWORK MODEL; PREPROCESSING SCHEME; ROLLING BEARINGS; TIME FREQUENCY DOMAIN;

FAULT DETECTION;

EID: 85019734822     PISSN: 00262714     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.microrel.2017.03.006     Document Type: Article

References (36)

1. Wang, D., Tse, P.W., Tsui, K.L., An enhanced Kurto-gram method for fault diagnosis of rolling element bearings. Mech. Syst. Signal Process. 35:1–2 (2013), 176–199.
2. Jie Liu, I.E.E.E., Wang, Wilson, Golnaraghi, Farid, An enhanced diagnostic scheme for bearing condition monitoring. IEEE Trans. Instrum. Meas. 59:2 (2010), 309–321.
3. Miao, Q., Zhou, Q.H., Planetary gearbox vibration signal characteristics analysis and fault diagnosis. Shock. Vib., 2015, 2015, 126489.
4. Wang, Y., He, Z., Xiang, J., Zi, Y., Application of local mean decomposition to the surveillance and diagnostics of low-speed helical gearbox. Mech. Mach. Theory 47:1 (2012), 62–73.
5. Lei, Y., Kong, D., Lin, J., Zuo, M.J., Fault detection of planetary gearboxes using new diagnostic parameters. Meas. Sci. Technol., 23(5), 2012, 055605.
6. Hou, S., Liang, M., Zhang, Y., Li, C., Vibration signal demodulation and bearing fault detection: a clustering-based segmentation method. Proc. IME C J. Mech. Eng. Sci. 228:11 (2014), 1888–1899.
7. Jardine, A.K.S., Lin, D., Banjevic, D., A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech. Syst. Signal Process. 20:7 (2006), 1483–1510.
8. Guo, L., Chen, J., Li, X., Rolling bearing fault classification based on envelope spectrum and support vector machine. J. Vib. Control. 15:9 (2009), 1349–1363.
9. Miao, Q., Tang, C., Liang, W., Pecht, M., Health assessment of cooling fan bearings using wavelet-based filtering. Sensors 13:1 (2013), 274–291.
10. Duda, R., Hart, P., Stork, D., Pattern Classification. 2001, Wiley, Hoboken, NJ.
11. Jack, L.B., Nandi, A.K., McCormick, A.C., Diagnosis of rolling element bearing faults using radial basis function networks. Appl. Signal Process. 6 (1999), 25–32.
12. Samanta, B., Al-Balushi, K.R., Artificial neural-network-based fault diagnostics of rolling-element bearings using time-domain features. Mech. Syst. Signal Process. 17:2 (Mar. 2003), 317–328.
13. Isermann, R., On fuzzy logic applications for automatic control, supervision, and fault diagnosis. IEEE Trans. Syst. Man Cybern. Syst. Humans 28:2 (Mar. 1998), 221–235.
14. Castellano, G., Fanelli, A., Mencar, C., An empirical risk functional to improve learning in a neuro fuzzy classifier. IEEE Trans. Syst. Man Cybern. B Cybern. 34:1 (Feb. 2004), 725–731.
15. Uluyol, O., Kim, K., Nwadiogbu, E., Synergistic use of soft computing technologies for fault detection in gas turbine engines. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 36:6 (Jul. 2006), 476–484.
16. Wang, W., An intelligent system for machinery condition monitoring. IEEE Trans. Fuzzy Syst. 16:1 (Feb. 2008), 110–122.
17. Wang, W., An enhanced diagnostic system for gear system monitoring. IEEE Trans. Syst. Man Cybern. B Cybern. 38:1 (Feb. 2008), 102–112.
18. Wang, D., Miao, Q., Smoothness index-guided Bayesian inference for determining joint posterior probability distributions of anti-symmetric real Laplace wavelet parameters for identification of different bearing faults. J. Sound Vib. 345:9 (2015), 250–266.
19. Wang, D., Miao, Q., Kang, R., Robust health evaluation of gearbox subject to tooth failure with wavelet decomposition. J. Sound Vib. 324:3–5 (2009), 1141–1157.
20. Miao, Q., Makis, V., Condition monitoring and classification of rotating machinery using wavelets and hidden Markov models. Mech. Syst. Signal Process. 21:2 (2007), 840–855.
21. Ali, J.B., Fnaiech, N., Saidi, L., Chebel-Morello, B., Fnaiech, F., Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals. Appl. Acoust. 89 (2015), 16–27.
22. Li, Y., Xu, M., Wei, Y., Huang, W., Bearing fault diagnosis based on adaptive multiscale fuzzy entropy and support vector machine. J. Vibroeng. 17:3 (2015), 1188–1202.
23. Souzal, D.L., Granzotto, M.H., Almeida, G.M., Oliveira-Lopes, L.C., Fault detection and diagnosis using support vector machines - a SVC and SVR comparison. J. Saf. Eng. 3:1 (2014), 18–29.
24. Miao, Q., Xie, L., Cui, H., Liang, W., Pecht, M., Remaining useful life prediction of Lithium-ion battery with unscented particle filter technique. Microelectron. Reliab. 53:6 (2013), 805–810.
25. Jia, Feng, Lei, Yaguo, Lin, Jing, Zhou, Xin, Lu, Na, Deep neural networks: a promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mech. Syst. Signal Process. 72-73 (2016), 303–315.
26. Chen, Z.Q., Li, C., Sanchez, R.V., Gearbox fault identification and classification with convolutional neural networks. Shock. Vib. 2015:2 (2015), 1–10.
27. Tran, V.T., Thobiani, F.A., Ball, A., An approach to fault diagnosis of reciprocating compressor valves using Teager–Kaiser energy operator and deep belief networks. Expert Syst. Appl. 41:9 (2014), 4113–4122.
28. Li, C., Sanchez, R., Zurita, G., Cerrada, M., Cabrera, D., Vásquez, R., Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis. Neurocomputing 168 (2015), 119–127.
29. Chen, Z., Li, C., Sánchez, R.V., Multi-layer neural network with deep belief network for gearbox fault diagnosis. J. Vibroeng. 17:5 (2015), 2379–2392.
30. Salakhutdinov, R., Hinton, G., Deep Boltzmann machines. J. Mach. Learn. Res. 5:2 (2009), 448–455.
31. Hinton, G.E., Osindero, S., Teh, Y., A fast learning algorithm for deep belief nets. Neural Comput. 18 (2006), 1527–1554.
32. Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A., Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11 (2010), 3371–3408.
33. Freund, Y., Haussler, D., Unsupervised learning of distributions on binary vectors using two layer networks. Technical Report UCSC-CRL-94-25, 1994, University of California, Santa Cruz.
34. Salakhutdinov, R., Hinton, G., An efficient learning procedure for deep machines. Neural Comput. 24:8 (2012), 1967–2006.
35. Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H., Greedy layer-wise training of deep networks. Schölkopf, B., Platt, J., Hoffman, T., (eds.) Advances in Neural Information Processing Systems 19 (NIPS’06), 2007, MIT Press, 153–160.
36. Bengio, Y., Learning deep architectures for AI. Found. Trends Mach. Learn. 2:1 (2009), 1–127.