A tutorial for model-based prognostics algorithms based on Matlab code

Proceedings of the Annual Conference of the Prognostics and Health Management Society 2012, PHM 2012

Volumn , Issue , 2012, Pages 224-232

  An, Dawn ^a   Choi, Joo Ho ^a   Kim, Nam Ho ^b  

^a Hankuk Aviation University   (South Korea)

^b UNIVERSITY OF FLORIDA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; SYSTEMS ENGINEERING;

BATTERY DEGRADATION; CRACK GROWTH MODEL; DAMAGE PROGRESSION; DEGRADATION MODEL; MODEL PARAMETERS; PREDICTION INTERVAL; REMAINING USEFUL LIVES; RUL PREDICTIONS;

MATLAB;

EID: 84920535768     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. An, D., Choi, J. H., Schmitz, T. L., & Kim, N. H., (2011). In-Situ Monitoring and Prediction of Progressive Joint Wear using Bayesian Statistics, Wear, vol. 270(11-12), pp. 828-838.
2. An, D., Choi, J. H., & Kim, N. H., (2012). Identification of Correlated Damage Parameters under Noise and Bias Using Bayesian Inference. Structural Health Monitoring, vol. 11(3), pp. 292-302.
3. Bayes, T., (1763). An Essay towards solving a problem in the doctrine of chances, Philosophical Transactions of the Royal Society of London, vol. 53, pp. 370-418.
4. Daigle, M., & Goebel, K., (2011). Multiple Damage Progression Paths in Model-based Prognostics. Aerospace Conference, 2011 IEEE.
5. DeCastro, J. A., Tang, L., Loparo, K. A., Goebel, K., and Vachtsevanos, G., (2009). Exact Nonlinear Filtering and Prediction in Process Model-based Prognostics. Annual Conference of the Prognostics and Health Management Society.
6. Goebel, K., Saha, B., Saxena, A., Celaya, J. R., & Christophersen, J., (2008). Prognostics in Battery Health Management. IEEE Instrumentation and Measurements Magazine, vol. 11(4), pp. 33-40.
7. Kalman, R. E., (1960). A New Approach to Linear Filtering and Prediction Problems. Transaction of the ASME-Journal of Basic Engineering, vol. 82(1), pp. 35-45.
8. Luo, J., Pattipati, K.R., Qiao, L., & Chigusa, S., (2008). Model-based Prognostic Techniques Applied to a Suspension System. IEEE Transactions on System, Man and Cybernetics, vol. 38(5), pp. 1156-1168.
9. Orchard, M. E., & Vachtsevanos, G. J., (2007). A Particle Filtering Approach for On-Line Failure Prognosis in a Planetary Carrier Plate. International Journal of Fuzzy Logic and Intelligent Systems, vol. 7(4), pp. 221-227.
10. Paris, P. C. & Erdogan, F., (1963). A Critical Analysis of Crack Propagation Laws, ASME Journal of Basic Engineering, vol. 85, pp. 528-534.
11. Payne, S. J., (2005). A Bayesian Approach for the Estimation of Model Parameters from Noisy Data Sets. IEEE Signal Processing Letters, vol. 12(8), pp. 553-556.
12. Zio, E., & Peloni, G., (2011). Particle Filtering Prognostic Estimation of the Remaining Useful Life of Nonlinear Components. Reliability Engineering and System Safety, vol. 96(3), pp. 403-409.
13. Li, P., Goodall, R. & Kadirkamanathan, V., (2003). Parameter Estimation of Railway Vehicle Dynamic Model using Rao-Blackwellised Particle Filter, European Control Conference.
14. Pitt, M. K., Silva, R. S., Giordani, P., & Kohn, R., (2012). On Some Properties of Markov Chain Monte Carlo Simulation Methods based on the Particle Filter. Journal of Econometrics, In Press (available online, July 2012).