Machine condition prediction based on adaptive neuro-fuzzy and high-order particle filtering

IEEE Transactions on Industrial Electronics

Volumn 58, Issue 9, 2011, Pages 4353-4364

  Chen, Chaochao ^a   Zhang, Bin ^b   Vachtsevanos, George ^a   Orchard, Marcos ^c  

^a GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

^b IMPACT TECHNOLOGIES LLC   (United States)

^c UNIVERSITY OF CHILE   (Chile)

Author keywords

Fuzzy systems; hidden Markov model (HMM); high order particle filter; machinery condition monitoring; neural networks; prognosis

Indexed keywords

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM; CARRIER PLATES; CATASTROPHIC FAILURES; CLASSICAL CONDITIONS; CONDITION BASED MAINTENANCE; CONDITION PREDICTION; FAILURE DATA; FAULT INDICATORS; FAULT PROPAGATION; FAULTY BEARINGS; HIDDEN MARKOV MODEL (HMM); HIGH-ORDER; MACHINE DYNAMICS; MACHINERY CONDITION MONITORING; MARKOV MODEL; NEURO-FUZZY; PARTICLE FILTER; PARTICLE FILTERING; PROGNOSIS; TESTING DATA; TIME EVOLUTIONS;

CONDITION MONITORING; FUZZY SYSTEMS; HIDDEN MARKOV MODELS; MACHINERY; MAINTENANCE; NEURAL NETWORKS; NONLINEAR FILTERING; PROBABILITY DENSITY FUNCTION;

FUZZY INFERENCE;

EID: 80051722734     PISSN: 02780046     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIE.2010.2098369     Document Type: Article

References (33)

1. G. Vachtsevanos, F. Lewis, M. Roemer, A. Hess, and B. Wu, Intelligent Fault Diagnosis and Prognosis for Engineering Systems. Hoboken, NJ: Wiley, 2006.
2. N. Gebraeel, M. Lawley, R. Liu, and V. Parmeshwaran, "Residual life predictions from vibration-based degradation signals: A neural network approach", IEEE Trans. Ind. Electron., vol. 51, no. 3, pp. 694-700, Jun. 2004.
3. Y. Chen, H. Wu, M. Chou, and K. Lee, "Online failure prediction of the electrolytic capacitor for LC filter of switching-mode power converters", IEEE Trans. Ind. Electron., vol. 55, no. 1, pp. 400-406, Jan. 2008.
4. Y. Xiong, X. Cheng, Z. Shen, C. Mi, H. Wu, and V. Garg, "Prognostic and warning system for power-electronic modules in electric, hybrid electric, and fuel-cell vehicles", IEEE Trans. Ind. Electron., vol. 55, no. 6, pp. 2268-2276, Jun. 2008.
5. E. Strangas, S. Aviyente, and S. Zaidi, "Time-frequency analysis for efficient fault diagnosis and failure prognosis for interior permanent-magnet ac motors", IEEE Trans. Ind. Electron., vol. 55, no. 12, pp. 4191-4199, Dec. 2008.
6. P. Tse and D. Atherton, "Prediction of machine deterioration using vibration based fault trends and recurrent neural networks", J. Vib. Acoust., vol. 121, no. 3, pp. 355-362, Jul. 1999.
7. F. Zhao, J. Chen, L. Guo, and X. Lin, "Neuro-fuzzy based condition prediction of bearing health", J. Vib. Control, vol. 15, no. 7, pp. 1079-1091, Jul. 2009.
8. W. Wang, F. Golnaraghi, and F. Ismail, "Prognosis of machine health condition using neuro-fuzzy systems", Mech. Syst. Signal Process., vol. 18, no. 4, pp. 813-831, Jul. 2004.
9. J. Liu, W. Wang, and F. Golnaraghi, "A multi-step predictor with a variable input pattern for system state forecasting", Mech. Syst. Signal Process., vol. 23, no. 5, pp. 1586-1599, Jul. 2009.
10. P. Wang and G. Vachtsevanos, "Fault prognostics using dynamic wavelet neural networks", Artif. Intell. Eng. Des. Anal. Manuf., vol. 15, no. 4, pp. 349-365, Sep. 2001. (Pubitemid 32975394)
11. G. Wen and X. Zhang, "Prediction method of machinery condition based on recurrent neural networks models", J. Appl. Sci., vol. 4, no. 4, pp. 675-679, 2004.
12. J. P. Cusumano, D. Chelidze, and A. Chattterjee, "A dynamical systems approach to damage evolution tracking, part 2: Model-based validation and interpretation", J. Vib. Acoust., vol. 124, no. 2, pp. 258-264, Apr. 2002. (Pubitemid 35202691)
13. B. Samanta and C. Nataraj, "Prognostics of machine condition using soft computing", Robot. Comput.-Integr. Manuf., vol. 24, no. 6, pp. 816-823, Dec. 2008.
14. G. Niu and B. Yang, "Dempster-Shafer regression for multi-step-ahead time-series prediction towards data-driven machinery prognosis", Mech. Syst. Signal Process., vol. 23, no. 3, pp. 740-751, Apr. 2009.
15. V. Tran, B. Yang, and A. Tan, "Multi-step ahead direct prediction for machine condition prognosis using regression trees and neuro-fuzzy systems", Expert Syst. Appl., vol. 36, no. 5, pp. 9378-9387, Jul. 2009.
16. W. Wang, "An adaptive predictor for dynamic system forecasting", Mech. Syst. Signal Process., vol. 21, no. 2, pp. 809-823, Feb. 2007. (Pubitemid 44667411)
17. J. Lee, "A systematic approach for developing and deploying advanced prognostics technologies and tools: Methodology and application", in Proc. 2nd World Congr. Eng. Asset Manage., Harrogate, U. K., 2007, pp. 1195-1206.
18. A. K. S. Jardine, D. Lin, and D. Banjevic, "A review on machinery diagnostics and prognostics implementing condition-based maintenance", Mech. Syst. Signal Process., vol. 20, no. 7, pp. 1483-1510, Oct. 2006.
19. C. Andrieu, A. Doucet, and E. Punskaya, "Sequential Monte Carlo methods for optimal filtering", in Sequential Monte Carlo Methods in Practice, A. Doucet, N. de Freitas, and N. Gordon, Eds. New York: Springer-Verlag, 2001.
20. M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, "A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking", IEEE Trans. Signal Process., vol. 50, no. 2, pp. 174-188, Feb. 2002. (Pubitemid 34291500)
21. A. Doucet, S. Godstill, and C. Andrieu, "On sequential Monte Carlo sampling methods for Bayesian filtering", Stat. Comput., vol. 10, no. 3, pp. 197-208, Jul. 2000.
22. M. Orchard, "A particle filtering-based framework for on-line fault diagnosis and failure prognosis", Ph. D. dissertation, Georgia Inst. Technol., Atlanta, GA, 2007.
23. M. Orchard and G. Vachtsevanos, "A particle filtering approach for on-line fault diagnosis and failure prognosis", Trans. Inst. Meas. Control, vol. 31, no. 3/4, pp. 221-246, Jun. 2009.
24. B. Saha, K. Goebel, S. Poll, and J. Christophersen, "Prognostics methods for battery health monitoring using a Bayesian framework", IEEE Trans. Instrum. Meas., vol. 58, no. 2, pp. 291-296, Feb. 2009.
25. J.-S. R. Jang, C.-T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing. Upper Saddle River, NJ: Prentice-Hall, 1997.
26. J. Whittaker, Graphical Models in Applied Mathematical Multivariate Statistics. Chichester, U. K.: Wiley, 1990.
27. B. Zhang, T. Khawaja, R. Patrick, G. Vachtsevanos, M. Orchard, and A. Saxena, "Application of blind deconvolution denoising in failure prognosis", IEEE Trans. Instrum. Meas., vol. 58, no. 2, pp. 303-310, Feb. 2009.
28. B. Zhang, T. Khawaja, R. Patrick, and G. Vachtsevanos, "Blind deconvolution denoising for helicopter vibration signals", IEEE/ASME Trans. Mechatronics, vol. 13, no. 5, pp. 558-565, Oct. 2008.
29. S. Zaidi, S. Aviyente, M. Salman, K. Shin, and E. Strangas, "Prognosis of gear failure in dc starter motors using hidden Markov models", IEEE Trans. Ind. Electron., vol. 58, no. 5, pp. 1695-1706, May 2011.
30. W. Caesarendra, G. Niu, and B. Yang, "Machine condition prognosis based on sequential Monte Carlo method", Expert Syst. Appl., vol. 37, no. 3, pp. 2412-2420, Mar. 2010.
31. F.-J. Lin, W.-J. Hwang, and R.-J. Wai, "A supervisory fuzzy neural network control system for tracking periodic inputs", IEEE Trans. Fuzzy Syst., vol. 7, no. 1, pp. 41-52, Feb. 1999.
32. J. Mendel, Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Upper Saddle River, NJ: Prentice-Hall, 2001.
33. I. Morgan, H. Liu, B. Tormos, and A. Sala, "Detection and diagnosis of incipient faults in heavy-duty diesel engines", IEEE Trans. Ind. Electron., vol. 57, no. 10, pp. 3522-3532, Oct. 2010.