Machine remaining useful life prediction based on adaptive Neuro-Fuzzy and High-Order particle filtering

Annual Conference of the Prognostics and Health Management Society, PHM 2010

Volumn , Issue , 2010, Pages

  Chen, Chaochao ^a   Vachtsevanos, George ^a   Orchard, Marcos E ^b  

^a GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

^b UNIVERSITY OF CHILE   (Chile)

Author keywords

[No Author keywords available]

Indexed keywords

FORECASTING; FUZZY INFERENCE; FUZZY SYSTEMS; MAINTENANCE; MONTE CARLO METHODS; PROBABILITY DENSITY FUNCTION;

ADAPTIVE NEURO-FUZZY; ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM; CATASTROPHIC FAILURES; CONDITION-BASED MAINTENANCE; MACHINE REMAINING USEFUL LIVES; PARTICLE FILTERING; PREDICTION ACCURACY; PROBABILITY DENSITY FUNCTION (PDF);

SIGNAL FILTERING AND PREDICTION;

EID: 84920528507     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. Vachtsevanos, G., Lewis, F. L., Roemer, M., Hess, A. & Wu, B. (2006). Intelligent Fault Diagnosis and Prognosis for Engineering Systems, 1st ed. Hoboken, New Jersey: John Wiley & Sons, Inc, .
2. Gebraeel, N., Lawley, M., Liu, R., Parmeshwaran, V. (2004). Residual life predictions from vibrationbased degradation signals: a neural network approach, IEEE Transactions on Industrial Electronics, vol. 51, pp. 694-700.
3. Tse, P., Atherton, D. (1999). Prediction of machine deterioration using vibration based fault trends and recurrent neural networks, Journal of Vibration and Acoustics, vol. 121, pp.355-362.
4. Zhao, F., Chen, J., Guo, L., Lin, X. (2009). Neurofuzzy based condition prediction of bearing health, Journal of Vibration and Control, vol. 15, pp. 1079-1091.
5. Wang, W., Golnaraghi, F., Ismail, F. (2004). Prognosis of machine health condition using neuro-fuzzy systems, Mechanical System and Signal Processing, vol. 18, pp. 813-831.
6. Liu, J., Wang, W., Golnaraghi, F. (2009). A multi-step predictor with a variable input pattern for system state forecasting, Mechanical System and Signal Processing, vol. 23, pp. 1586-1599.
7. Wang, P., Vachtsevanos, G. (2001). Fault prognostics using dynamic wavelet neural networks, Artificial Intelligence for Engineering Design, Analysis and Manufacturing, vol. 15, pp. 349-365.
8. Samanta, B., Nataraj, C. (2008). Prognostics of machine condition using soft computing, Robotics and Computer-Integrated Manufacturing., vol. 24, pp. 816-823.
9. Tran, V., Yang, B., Tan, A. (2009). Multi-step ahead direct prediction for machine condition prognosis using regression trees and neuro-fuzzy systems, Expert System with Appication, vol. 36, pp. 9378-9387.
10. Wang, W. (2007). An adaptive predictor for dynamic system forecasting, Mechanical System and Signal Processing, vol. 21, pp. 809-823.
11. Jardine, A.K.S., Lin, D., Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance, Mechanical System and Signal Processing, vol. 20, pp. 1483-1510.
12. Arulampalam, M.S., Maskell, S., Gordon, N., Clapp T. (2002). A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE Transactions on Signal Processing., vol. 50, pp.174-188.
13. Orchard, M., and Vachtsevanos, G. (2009). "A Particle Filtering Approach for On-Line Fault Diagnosis and Failure Prognosis, Transactions of the Institute of Measurement and Control, vol. 31, no. 3-4, pp. 221-246.
14. Jang, J.-S. R., Sun, C.-T., Mizutani, E. (1997). Neuro-Fuzzy and Soft Computing. NJ: Prentice-Hall PTR.
15. Whittaker, J. (1990) Graphical Models in Applied Mathematical Multivariate Statistics, United Kingdom: John Wiley & Sons.
16. Tian, Z. Wong, L. Safaei, N. (2009). A neural network approach for remaining useful life prediction utilizing both failure and suspension histories, Mechanical System and Signal Processing doi:10.1016/j.ymssp.2009.11.005.
17. Zhang, B. Khawaja, T. Patrick, R. Vachtsevanos, G. (2008). Blind deconvolution denoising for helicopter vibration signals, IEEE/ASME Transactions on Mechatronics, vol. 13, pp. 558-565.