Improving Computational Efficiency of Prediction in Model-based Prognostics Using the Unscented Transform
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Abstract
Model-based prognostics captures system knowledge in the form of physics-based models of components, and how they fail, in order to obtain accurate predictions of end of life (EOL). EOL is predicted based on the estimated current state distribution of a component and expected profiles of future usage. In general, this requires simulations of the component using the underlying models. In this paper, we develop a simulation-based prediction methodology that achieves computational efficiency by performing only the minimal number of simulations needed in order to accurately approximate the mean and variance of the complete EOL distribution. This is performed through the use of the unscented transform, which predicts the means and covariances of a distribution passed through a nonlinear transformation. In this case, the EOL simulation acts as that nonlinear transformation. In this paper, we review the unscented transform, and describe how this concept is applied to efficient EOL prediction. As a case study, we develop a physics-based model of a solenoid valve, and perform simulation experiments to demonstrate improved computational efficiency without sacrificing prediction accuracy
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model-based prognostics, particle filters, unscented transform, solenoid valve
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, 50(2), 174– 188.
Byington, C. S., Watson, M., Edwards, D., & Stoelting, P. (2004, March). A model-based approach to prognostics and health management for flight control actuators. In Proceedings of the 2004 IEEE Aerospace Conference (Vol. 6, pp. 3551–3562).
Cappe, O., Godsill, S. J., & Moulines, E. (2007). An overview of existing methods and recent advances in sequential Monte Carlo. Proceedings of the IEEE, 95(5), 899.
Daigle, M., & Goebel, K. (2009, September). Model- based prognostics with fixed-lag particle filters. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2009.
Daigle, M., & Goebel, K. (2010, March). Model-based prognostics under limited sensing. In Proceedings of the 2010 IEEE Aerospace Conference.
Doucet, A., Godsill, S., & Andrieu, C. (2000). On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing, 10, 197–208.
Hutchings, I. M. (1992). Tribology: friction and wear of engineering materials. CRC Press.
Julier, S. J. (2002, November). The scaled unscented transformation. In Proceedings of the 2002 American Control Conference (Vol. 6, pp. 4555–4559).
Julier, S. J. (2003, June). The spherical simplex unscented transformation. In Proceedings of the 2003 American Control Conference (Vol. 3, p. 2430-2434).
Julier, S. J., & Uhlmann, J. K. (1997). A new extension of the Kalman filter to nonlinear systems. In Proceedings of the 11th International Symposium on Aerospace/Defense Sensing, Simulation and Controls (pp. 182–193).
Julier, S. J., & Uhlmann, J. K. (2002, November). Reduced sigma point filters for the propagation of means and covariances through nonlinear transformations. In Proceedings of the 2002 American Control Conference (Vol. 2, pp. 887–892).
Julier, S. J., & Uhlmann, J. K. (2004, March). Unscented filtering and nonlinear estimation. Proceedings of the IEEE, 92(3), 401–422.
Kitagawa, G. (1996). Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. Journal of Computational and Graphical Statistics, 5(1), 1–25.
Lyshevski, S. E., Sinha, A. S. C., & Seger, J. P. (1999, June). Modeling and control of turbocharged diesels for medium and heavy vehicles. In Proceedings of the American Control Conference.
Orchard, M., Kacprzynski, G., Goebel, K., Saha, B., & Vachtsevanos, G. (2008, October). Advances in uncertainty representation and management for particle filtering applied to prognostics. In Proceedings of International Conference on Prognostics and Health Management.
Rahman, M. F., Cheung, N. C., & Lim, K. W. (1996, September). Modelling of a nonlinear solenoid towards the development of a proportional actuator. In Proceedings of the 5th International Conference on Modelling and Simulation of Electrical Machines, Convertors, and Systems, ELECTRI- MACS (p. 121-128).
Roemer, M., Byington, C., Kacprzynski, G., & Vachtse- vanos, G. (2005). An overview of selected prognostic technologies with reference to an integrated PHM architecture. In Proceedings of the First International Forum on Integrated System Health Engineering and Management in Aerospace.
Saha, B., & Goebel, K. (2009, September). Modeling Li- ion battery capacity depletion in a particle filtering framework. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2009.
Saxena, A., Celaya, J., Balaban, E., Goebel, K., Saha, B., Saha, S., et al. (2008, Oct). Metrics for evaluating performance of prognostic techniques. In International Conference on Prognostics and Health Management 2008.
Saxena, A., Celaya, J., Saha, B., Saha, S., & Goebel, K. (2009, September). On Applying the Prognostic Performance Metrics. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2009.
Szente, V., & Vad, J. (2001). Computational and experimental investigation on solenoid valve dynamics. In Proceedings of the 2001 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (Vol. 1, p. 618 -623).
Tansel, I. N., Perotti, J. M., Yenilmez, A., & Chen, P. (2005). Valve health monitoring with wavelet transformation and neural networks (WT-NN). In 2005 ICSC Congress on Computational Intelligence Methods and Applications.
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