Particle Filtering-Based System Degradation Prediction Applied to Jet Engines



Peng Wang Robert X. Gao


This paper investigates a real-time fault detection and degradation prediction scheme for dynamical systems such as jet engines, based on Regularized Particle Filtering (RPF). Particle Filtering is a prognosis method for the prediction of state degradation and remaining useful life (RUL) due to its demonstrated performance in handling non-linear and non-Gaussian situations. RPF overcomes the problem of sample impoverishment among particles over the resampling process. Based on measured data from hybrid sensing and nonlinear models, which link system parameters and degradation state to the measurement, RPF has been applied to establishing a framework for both state and parameter estimation, to achieve prognosis at the component level. In addition, a modified system evolution model is proposed to track both exponential and transient types of system performance degradation. The developed method is evaluated using simulated data created with C- MAPSS, which contains measured parameters associated with engine degradation under nominal and varied fault types (fan, compressor and turbine) during a series of flights. The developed system-parameter estimation method is found effective in state estimation and degradation prediction in jet engines.

How to Cite

Wang , P. ., & X. Gao, R. . (2014). Particle Filtering-Based System Degradation Prediction Applied to Jet Engines. Annual Conference of the PHM Society, 6(1).
Abstract 84 | PDF Downloads 69



health monitoring, Model-based Prognostics, Parameter Estimation, Particle Filtering

Doucet A., & Johansen A. (2009). A Tutorial on Particle Filtering and Smoothing: Fifteen Years Later. In Crisan D., & Rozovsky B., The Oxford Handbook of Nonlinear Filtering, (656-704). Oxford: Oxford University Press.

Daroogheh, N., Meskin, N., & Khorasani, K. (2013). Particle filtering for state and parameter estimation in gas turbine engine fault diagnostics. Proceedings of 2013 American
Control Conference. June 17-19, Washington, DC, USA.

Gordon, N. J., Salmond, D., & Smith, A. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings-F Radar and Signal Processing, vol. 140(2), pp. 107-113. DOI: 10.1049/ip-f- 2.1993.0015.

Julier, S., & Uhlmann, J.,(1997). A new extension of Kalman Filter to nonlinear systems. Proceedings of 11th International Symposium on Aerospace/Defense sensing, Simulation and Controls, Multi Sensor Fusion, Tracking and Resource Management. July 28, Orlando, FL, USA. doi: 10.1117/12.280797

Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Transactions of the ASME- Journal of Basic Engineering, vol. 82, pp. 35-45. doi: 10.1115/1.3662552

Liu, J., & West, M. (2001). Combined parameter and state estimation in simulation based filtering. In Doucet, A., Freitas, N., & Gordon, N., Sequential Monte Carlo Methods in Practice, (197-223). NY: Springer New York.

Moran, M., & Howard, N. S. (2004). Fundamentals of Engineering Thermodynamics. NJ: Wiely.

Musso, C., & Oudjane, N. (2001). Improving regularized particle filters. In Doucet, A., Freitas, N., & Gordon, N., Sequential Monte Carlo Methods in Practice, (197- 223). NY: Springer New York.

Orchard, M.E., Cerda, M., Olivares, B., & Silva, J. (2012). Sequential Monte Carlo methods for Discharge Time Prognosis in Lithium-Ion Batteries. International Journal of Prognostics and Health Management, vol.
3(2) 010, pp. 1-12.

Peng, Y., Dong, M., & Zuo, M. J. (2010). Current status of machine prognostics in condition-based maintenance: a review. International Journal of Advanced Manufacturing Technology. vol. 50, pp. 297-313. doi: 10.1007/s00170-009-2482-0

Saxena, A., Goebel, K., Simon, D., & Eklund, Neil. (2008).Damage propagation modeling for aircraft engine run- to-failure simulation. Proceedings of International Conference on Prognostics and Health Management. October 6-9, Denver, CO. doi: 10.1109/PHM.2008.4711414

Volponi, A. J. (2003). Foundation of gas path analysis. In Mathloudakls, K., & Sieverding, C. H., GasTurbine Condition Monitoring and Fault Diagnosis, (1-16). Belgium: von Karman Institute.

Wang, J., Wang, P., & Gao, R. (2013). Tool life prediction for sustainable manufacturing. Proceedings of 11th Global Conference on Sustainable Manufacturing, September 23-25, Berlin, Germany.

Zhu, J., Yoon, J., He, D., Qu, Y., & Bechhoefer, E. (2013). Lubrication Oil Condition Monitoring and Remaining Useful Life Prediction With Particle Filtering.
International Journal of Prognostics and Health Management: vol. 4 020, pp. 1-15.
Technical Research Papers