Multi-steps degradation process prediction for bearing based on improved back propagation neural network

Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science

Volumn 227, Issue 7, 2013, Pages 1544-1553

  Mi, Lin ^a   Tan, Wei ^a   Chen, Ran ^a  

^a CHONGQING UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Bearing; Degradation process prediction; Improved back propagation neural network; Principal component analysis

Indexed keywords

BACK PROPAGATION NEURAL NETWORKS; DEGRADATION PREDICTIONS; DEGRADATION PROCESS; EMBEDDING DIMENSIONS; IMPROVED BACK PROPAGATION NEURAL NETWORK; MULTI-FEATURES FUSIONS; SPACE CONSTRUCTIONS; TIME FREQUENCY DOMAIN;

BEARINGS (STRUCTURAL); FORECASTING; PHASE SPACE METHODS; PRINCIPAL COMPONENT ANALYSIS;

NEURAL NETWORKS;

EID: 84883383725     PISSN: 09544062     EISSN: 20412983     Source Type: Journal    
DOI: 10.1177/0954406212462520     Document Type: Article

References (29)

1. Mahamad AK, Saon S and Hiyama T. Predicting remaining useful life of rotating machinery based artificial neural network. Comput Math Appl 2010; 60: 1078-1087.
2. Du S, Lv J and Xi L. Degradation process prediction for rotational machinery based on hybrid intelligent model. Robot Comput Integr Manuf 2012; 28(2): 190-207.
3. Qiu H, Lee J, Lin J, et al. Robust performance degradation assessment methods for enhanced rolling element bearing prognostics. Adv Eng Inform 2003; 17: 127-140.
4. Kar C and Mohanty AR. Multistage gearbox condition monitoring using motor current signature analysis and Kolmogorov-Smirnov test. J Sound Vib 2006; 290: 337-368.
5. Gebraeel N, Lawley M, Liu R, et al. Residual life predictions from vibration-based degradation signals: a neural network approach. IEEE Trans Ind Electron 2004; 51: 694-700.
6. Huang R, Xi L, Li X, et al. Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods. Mech Syst Signal Process 2007; 21: 193-207.
7. Ocak H, Loparo KA and Discenzo FM. Online tracking of bearing wear using wavelet packet decomposition and probabilistic modeling: a method for bearing prognostics. J Sound Vib 2007; 302: 951-961.
8. Malhi A and Gao RX. PCA-based feature selection scheme for machine defect classification. IEEE Trans Instrum Meas 2004; 53: 1517-1525.
9. Liao L and Lee J. A novel method for machine performance degradation assessment based on Exed cycle features test. J Sound Vib 2009; 326: 894-908.
10. Zhang S, Hodkiewicz M, Ma L, et al. Machinery condition prognosis using multivariate analysis. In: The first world congress of engineering asset management, Gold Coast, Australia, 2006, pp. 1-8.
11. Jardine AKS, Lin D and Banjevic D. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Process 2006; 20: 1483-1510.
12. Ghidini T and Donne C. Fatigue life prediction using fracture mechanics methods. Eng Fract Mech 2009; 75: 134-148.
13. Marble S and Morton BP. Predicting the remaining life of propulsion system bearings. In: Proceedings of the 2006 IEEE aerospace conference, Big Sky, MT, USA, 2006.
14. Tian Z, Wong L and Safaei N. A neural network approach for remaining useful life prediction utilizing both failure and suspension histories. Mech Syst Signal Process 2010; 24: 1542-1555.
15. Caesarendra W, Widodob A and Yang B-S. Combination of probability approach and support vector machine towards machine health prognostics. Probab Eng Mech 2011; 26: 165-173.
16. Lee J, Ni J, Djurdjanovic D, et al. Intelligent prognostics tools and e-maintenance. Comput Ind 2006; 57: 476-489.
17. Gebraeel N, Lawley MA and Liu R. Residual life, predictions from vibration-based degradation signals: a neural network approach. IEEE Trans Ind Electron 2004; 51(3): 694-700.
18. Li CH and Park SC. A novel algorithm for text categorization using improved back-propagation neural network. In: Proceedings of FSKD 2006, LNAI 4223, 2006, pp. 452-460.
19. Wang W and Yu B. Text categorization based on combination of modified back propagation neural network and latent semantic analysis. Neural Comput Appl 2009; 18: 875-881.
20. Xi F, Sun Q and Krishnappa G. Bearing diagnostics based on pattern recognition of statistical parameters. J Vibr Contr 2000; 6: 75-92.
21. Tran VT, Yanga B-S and Oh M-S. Machine condition prognosis based on regression trees and one-step-ahead prediction. Mech Syst Signal Process 2008; 22: 1179-1193.
22. Fraser AM and Swinney HL. Independent coordinates for strange attractors from mutual information. Phys Rev A 1986; 33: 1134-1140.
23. Cao L. Practical method for determining the minimum embedding dimension of a scalar time series. Physica D 1997; 110: 43-50.
24. Tran VT, Yang B-S and Tan ACC. Multi-step ahead direct prediction for the machine condition prognosis using regression trees and neuro-fuzzy systems. Exp Syst Appl 2009; 36: 9378-9387.
25. Marcellino M, Stock J and Watson MW. A comparison of direct and iterated multi-step AR methods for forecasting macroeconomic time series. J Econom 2006; 135: 499-526.
26. Takens F. Detecting strange attractors in turbulence. In: DA Rand and LS Young (eds) Dynamical systems and turbulence. New York: Springer-Verlag, 1981, pp. 366-381.
27. Lee J, Qiu H, Yu G, et al. Rexnord Technical Services, "Bearing Data Set". IMS, University of Cincinnati, NASA Ames Prognostics Data Repository. http://ti.arc.nasa.gov/project/prognosticdata-repository, NASA Ames, Moffett Field, CA (accessed 9 May 2009).
28. Kakar M, Seierstad T, Roe K, et al. Artificial neural networks for prediction of response to chemoradiation in ht29 xenografts. Int J Radiat Oncol Biol Phys 2009; 75(2): 506-511.
29. Sun C, Zhang ZS and He ZJ. Research on bearing life prediction based on support vector machine and its application. J Phys: Conf Ser 2011; 305: 1-9.