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Remaining Useful Life Prediction Using Elliptical Basis Function Network and Markov Chain
Abstract:This paper presents a novel method for remaining useful life prediction using the Elliptical Basis Function (EBF) network and a Markov chain. The EBF structure is trained by a modified Expectation-Maximization (EM) algorithm in order to take into account the missing covariate set. No explicit extrapolation is needed for internal covariates while a Markov chain is constructed to represent the evolution of external covariates in the study. The estimated external and the unknown internal covariates constitute an incomplete covariate set which are then used and analyzed by the EBF network to provide survival information of the asset. It is shown in the case study that the method slightly underestimates the remaining useful life of an asset which is a desirable result for early maintenance decision and resource planning.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1060932Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1323
 H. Liao, W. Zhao, and H. Guo "Predicting remaining useful life of an individual unit using proportional hazards model and logistic regression model," in 2006 Annual Reliability and Maintainability Symposium. RAMS '06., pp. 127-132.
 D. Kumar, B. Westberg, "Some reliability models for analysing the effects of operating conditions," International Journal of Reliability, Quality and Safety Engineering, vol. 4, pp. 133-148, 1997.
 D. R. Cox, "Regression models and life tables (with discussion)," Journal of the Royal Statistical Society. Series B (Methodological), vol. 34, no. 2, pp. 187-220, 1972.
 D. R. Cox, D. Oakes, Analysis of survival data. Chapman & Hall/CRC, 1984.
 Y. Sun, L. Ma, J. Mathew, W. Wang, and S. Zhang, "Mechanical systems hazard estimation using condition monitoring," Mechanical Systems and Signal Processing, vol. 20, no. 5, pp. 1189-1201, 2006.
 Y. Shao, K. Nezu, "Prognosis of remaining bearing life using neural networks," Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, vol. 214, no. 3, pp. 217-230, 2000.
 P. W. Tse, D. P. Atherton, "Prediction of machine deterioration using vibration based fault trends and recurrent neural networks," Journal of Vibration and Acoustics, vol. 121, no. 3, pp. 355-343, 1999.
 J. D. Kalbeisch, R. L. Prentice, The statistical analysis of failure time data. New York: Wiley, 1980.
 C. M. Bishop, Neural networks for pattern recognition. Oxford Univ Pr, 2005.
 Y. Yu, L. Ma, Y. Sun, and Y. Gu "Handling Incomplete Data In Survival Analysis With Multiple Covariates," in 2010 Proceedings of the 5rd World Congress on Engineering Asset Management and Intelligent Maintenance Systems., to be published.
 M. T. Musavi, W. Ahmed, K. H. Chan, K. B. Faris, and D. M. Hummels, "On the training of radial basis function classifiers," Neural Networks, vol. 5, no. 4, pp. 595-603, 1992.
 M. W. Mak, C.K. Li, "Elliptical basis function networks and radial basis function networks for speaker verification: A comparative study," in 1999 International Joint Conference on Neural Networks (IJCNN'99), pp. 3034-3039.
 A. P. Dempster, N.M. Laird, and D.B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," Journal of the Royal Statistical Society. Series B (Methodological), vol. 39, no. 1, pp. 1-38, 1977.
 G.J. McLachlan, T. Krishnan, The EM algorithm and extensions. New York: Wiley, 1997.
 Z. Ghahramani, M. I. Jordan, "Supervised learning from incomplete data via an EM approach," in Advances in neural information processing systems 6, J. D. Cowan, G. Tesauro, and J. Alspector, Ed. Morgan Kaufmann, 1995, pp. 120-127.
 W. Wang, "A model to predict the residual life of rolling element bearings given monitored condition information to date," IMA Journal of Management Mathematics, vol. 13, no. 1, pp. 3-16, 2002.
 B. Craig, P. Sendi, "Estimation of the transition matrix of a discrete-time Markov chain," Health Economics, vol. 11, no. 1, pp. 33-42, 2002.
 H. Yeh, W. Chan, E. Symanski, and B. Davis, "Estimating Transition Probabilities for Ignorable Intermittent Missing Data in a Discrete-Time Markov Chain," Communications in Statistics-Simulation and Computation, vol. 39, no. 2, pp. 433-448, 2010.
 R. Dybowski, "Classification of incomplete feature vectors by radial basis function networks," Pattern Recognition Letters, vol. 19, no. 14, pp. 1257-1264, 1998.
 PHM, Available from: http://www.phmconf.org/jOCS/index.php/phm/2008/challenge.2008.