International Journal of Fuzzy Logic and Intelligent Systems

Korean Institute of Intelligent Systems (한국지능시스템학회)
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A Particle Filtering Approach for On-Line Failure Prognosis in a Planetary Carrier Plate

  • Published : 2007.12.01
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Abstract

This paper introduces an on-line particle-filtering-based framework for failure prognosis in nonlinear, non-Gaussian systems. This framework uses a nonlinear state-space model of the plant(with unknown time-varying parameters) and a particle filtering(PF) algorithm to estimate the probability density function(pdf) of the state in real-time. The state pdf estimate is then used to predict the evolution in time of the fault indicator, obtaining as a result the pdf of the remaining useful life(RUL) for the faulty subsystem. This approach provides information about the precision and accuracy of long-term predictions, RUL expectations, and 95% confidence intervals for the condition under study. Data from a seeded fault test for a UH-60 planetary carrier plate are used to validate the proposed methodology.

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References

  1. Doucet, N. de Freitas, and N. Gordon, 'An introduction to Sequential Monte Carlo methods,' in Sequential Monte Carlo Methods in Practice, A. Doucet, N. de Freitas, and N. Gordon, Eds. New York: Springer-Verlag, 2001
  2. M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, 'A Tutorial on Particle Filters for Online Nonlinear/NonGaussian Bayesian Tracking,' IEEE Transactions on Signal Processing, vol. 50, no. 2, February 2002
  3. V. Kadirkamanathan, P. Li, M. H. Jaward, and S. G. Fabri, 'Particle filtering-based fault detection in non-linear stochastic systems,' International Journal of Systems Science, vol. 33, no. 4, pp. 259 - 265, 2002 https://doi.org/10.1080/00207720110102566
  4. X. Koutsoukos, J. Kurien, and F. Zhao, 'Monitoring and Diagnosis of Hybrid Systems Using Particle Filtering Models,' International Symposium on Mathematical Theory of Networks and Systems, 2002
  5. P. Li, and V. Kadirkamanathan, 'Particle Filtering Based Likelihood Ratio Approach to Fault Diagnosis in Nonlinear Stochastic Systems,' IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews, vol. 31, no. 3, 2001
  6. V. Verma, G. Gordon, R. Simmons, and S. Thrun, 'Particle Filters for Rover Fault Diagnosis,' IEEE Robotics & Automation Magazine, pp. 56 - 64, June 2004
  7. N. de Freitas, 'Rao-Blackwellised Particle Filtering for Fault Diagnosis,' IEEE Aerospace Conference Proceedings (Cat. No. 02TH8593), pt. 4, pp. 1767 - 1772, 2002
  8. A. J. Haug, 'A Tutorial on Bayesian Estimation and Tracking Techniques Applicable to Nonlinear and NonGaussian Processes,' MITRE Technical Report. MTR 05W0000004, The MITRE Corporation, 2005
  9. C. Andrieu, A. Doucet, and E. Punskaya, 'Sequential Monte Carlo Methods for Optimal Filtering,' in Sequential Monte Carlo Methods in Practice, A. Doucet, N. de Freitas, and N. Gordon, Eds. New York: Springer-Verlag, 2001
  10. A. Doucet, S. Godsill, and C. Andrieu, 'On Sequential Monte Carlo sampling methods for Bayesian filtering,' Statistics and Computing, vol. 10, no 3, pp. 197-208, July 2000 https://doi.org/10.1023/A:1008935410038
  11. A. Ray, and S. Tangirala, 'Stochastic Modeling of Fatigue Crack Dynamics for On-Line Failure Prognosis,' IEEE Transactions on Control Systems Technology, vol. 4, no. 4, pp. 443 - 451, 1996 https://doi.org/10.1109/87.508893
  12. R. Patrick, A Model Based Frameworkfor Fault Diagnosis and Prognosis of Dynamical Systems with an Application to Helicopter Transmissions, Ph.D. Thesis, Department of Electrical and Computer Engineering, Georgia institute of Technology, 2007
  13. M. Orchard, A Particle Filtering-based Frameworkfor Online Fault Diagnosis and Failure Prognosis, Ph.D. Thesis, Department of Electrical and Computer Engineering, Georgia institute of Technology, 2007

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