Machine degradation prognostic based on RVM and ARMA/GARCH model for bearing fault simulated data

2010 Prognostics and System Health Management Conference, PHM '10

Volumn , Issue , 2010, Pages

  Caesarendra, Wahyu ^a   Widodo, Achmad ^b   Thom, Pham Hong ^a   Yang, Bo Suk ^a  

^a Pukyong National University   (South Korea)

^b DIPONEGORO UNIVERSITY   (Indonesia)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVE AREA; BEARING FAULT; CENSORED DATA; FAILURE PROBABILITY; LOGISTIC REGRESSIONS; MULTI-STEP; PROGNOSTIC MODEL; RELEVANCE VECTOR MACHINE; SIMULATED DATA; TARGET VECTORS; TRAINING PROCESS;

BEARINGS (STRUCTURAL); ELECTRIC MACHINE THEORY; ELECTRIC REACTORS; FORECASTING; INTELLIGENT SYSTEMS; RAILROAD PLANT AND STRUCTURES;

DEGRADATION;

EID: 77950639883     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/PHM.2010.5414586     Document Type: Conference Paper

References (13)

1. Jardine, A.K.S., Lin, D. and Banjevic, D. "A Review on Machinery Diagnostics and Prognostics Implementing Condition-Based Maintenance," Mechanical Systems and Signal Processing, Vol.20 (2006), pp. 1483-1510.
2. Heng, A., Zhang, S., Tan, A.C.C. and Mathew, J. "Rotating Machinery Prognostics: State of the Art, Challenges and Opportunities," Mechanical Systems and Signal Processing, Vol.23 (2009), pp. 724-739.
3. Khotamasu, R., Huang, S.H. and Ver Dui, W.H. "System Health Monitoring and Prognostics - A Review of Current Paradigms and Practices," International Journal of Advanced Manufacturing and Technology, Vol.28 (1996), pp. 1012-1024.
4. Yan, J., Koc, M. and Lee, J. "A prognostic algorithm for machine performance assessment and its application," Production Planning and Control, Vol.15(8) (2004), pp. 796-801.
5. Tipping, M.E. et al, The relevance vector machine. In S. Solla. T. Leen, K.R. Muller: Advances in Neural Information Processing System. 12. MIT Press, Cambridge, MA, (2000), pp. 287-289.
6. 6. Tipping, M.E. "Spare Bayesian learning and the relevance vector machine," Journal of Machine Learning Research, Vol.1 (2001), pp. 211-244. (Pubitemid 33687203)
7. Scholkopf, B. and Smola, A.J. Learning with kernels: Support vector machines. Regularization. Optimization, and beyond. Cambridge, MA, MIT Press, (2002).
8. Pham, H.T. and Yang, B.S. "Estimation and forecasting of machine health condition using ARMA/GARCH model, "Mechanical Systems and Signal Processing (24) 2010, pp. 546-558.
9. McFadden, P.D. and Smith, J.D. "Model for the vibration produced by a single point defect in rolling element bearing," Journal of Sound and Vibration, Vol.96 (1984), pp. 69-82.
10. Wang, Y.F. and Kootsookos, P.J. "Modeling of low shaft speed bearing faults for condition monitoring," Mechanical Systems and Signal Processing, Vol.12(3) (1998), pp. 415-426.
11. 11. Gebraeel, N.Z., Lawley, M.A., Li, R. and Ryan, J.K. "Residual-life distribution from component degradation signals: A Bayesian approach," HE Transaction, Vol.37 (2005), pp. 543-557. (Pubitemid 40708863)
12. 12. Gebraeel, N. "Sensory-updated residual life distribution for components with exponential degradation pattern," IEEE Transactions on Automation Science and Engineering, Vol.3(4) (2006), pp. 382-393. (Pubitemid 44574332)
13. Caesarendra, W., Widodo, A. and Yang, B.S. "Application of relevance vector machine and logistic regression for machine degradation assessment," Mechanical Systems and Signal Processing, In Press.