Real-time spacecraft actuator fault diagnosis with state-segmented particle filtering

References

• Isermann R. Model-based fault-detection and diagnosis - Status and applications. Annu. Rev. Control. 2005;29:71–85.10.1016/j.arcontrol.2004.12.002
   Web of Science ®Google Scholar
• Marzat J, Piet-Lahanier H, Damongeot F, Walter E. Model-based fault diagnosis for aerospace systems: a survey. J. Aerosp. Eng. 2011;226:1329–1360.
   Web of Science ®Google Scholar
• Gustafsson F. Particle filter theory and practice with positioning applications. IEEE Aerosp. Electron.Syst. Mag. 2010;25:53–82.10.1109/MAES.2010.5546308
   Web of Science ®Google Scholar
• Rauch HE. Intelligent fault diagnosis and control reconfiguration. IEEE Control Syst. 1994;14:6–12.10.1109/37.291462
   Web of Science ®Google Scholar
• Isermann R, Ballé P. Trends in the application of model-based fault detection and diagnosis of technical processes. Control Eng. Pract. 1997;5:709–719.10.1016/S0967-0661(97)00053-1
   Web of Science ®Google Scholar
• Tafazoli M. A study of on-orbit spacecraft failures. Acta Astronaut. 2009;64:195–205.10.1016/j.actaastro.2008.07.019
   Web of Science ®Google Scholar
• Li P, Kadirkamanathan V. Particle filtering based likelihood ratio approach to fault diagnosis in nonlinear stochastic systems. IEEE Trans. Syst. Man Cybern. - Part C. 2001;31:337–343.
   Web of Science ®Google Scholar
• Andrieu C, Doucet A, Singh S, Tadic V. Particle methods for change detection. Syst. Ident., Control. 2004;92:423–438.
   Google Scholar
• Chen C, Zhang B, Vachtsevanos G, Orchard M. Machine condition prediction based on adaptive neuro-fuzzy and high-order particle filtering. IEEE Trans. Ind. Electron. 2011;58:4353–4364.10.1109/TIE.2010.2098369
   Web of Science ®Google Scholar
• Orchard M, Vachtsevanos G. A particle filtering approach for on-line fault diagnosis and failure prognosis. Trans. Inst. Meas. Control. 2003;31:221–246.
   Web of Science ®Google Scholar
• Grisetti G, Stachniss C, Burgard W. Improved techniques for grid mapping with Rao-Blackwellized particle filters. IEEE Trans. Rob. 2007;23:34–46.10.1109/TRO.2006.889486
   Web of Science ®Google Scholar
• de Freitas, N. Rao-Blackwellised particle filtering for fault diagnosis. In: 2002 IEEE Aerospace Conference Proceedings. 2002 Mar 9–16; Big Sky, MT, USA. Vol. 4. p. 4:1767–1772.
   Google Scholar
• Kobayashi T, Simon D. Evaluation of an enhanced bank of Kalman filters for in-flight aircraft engine sensor fault diagnostics. NASA/ARL, Technical Manual TM2004-213203, 2004.
   Google Scholar
• Larson EC, Parker BE Jr, Clark BR. Model-based sensor and actuator fault detection and isolation. Proceedings of the 2002 American Control Conference. 2002 May 8–10; Anchorage, AK, USA. Vol. 5. p. 4215–4219.
   Google Scholar
• Narasimhan V, Biswas G, Karsai G, Pasternak T, Zhao F. Building observers to handle fault isolation and control problems in hybrid systems. In: Proceedings of IEEE International Conference on Systems, Man, and Cybernetics. 2000 Oct 8–11; Nashville, TN, USA. Vol. 4. p. 2393–2398.
   Google Scholar
• Brumback B, Srinath M. A Chi-Square test for fault-detection in Kalman filters. IEEE Trans. Autom. Control. 1987;32:552–554.10.1109/TAC.1987.1104658
   Web of Science ®Google Scholar
• Alessandri A, Caccia M, Veruggio G. Fault detection of actuator faults in unmanned underwater vehicles. Control Eng. Pract. 1999;7:357–368.10.1016/S0967-0661(98)00169-5
   Web of Science ®Google Scholar
• Verma V, Thrun S, Simmons R. Variable resolution particle filter. In: The 18th International Joint Conference of Artificial Intelligence. Proceedings; 2003 Aug 9–15; Acapulco, Mexico. p. 976–981.
   Google Scholar
• Verma V. Tractable particle filters for robot fault diagnosis [PhD thesis]. Pittsburgh, PA, USA: Robotics Institute, Carnegie Mellon University; 2005.
   Google Scholar
• Thrun S, Langford J, Verma V. Risk sensitive particle filters. In: Dietterich TG, Becker S, Ghahramani Z, editors. Advances in neural information processing systems 14. vol. 2. Cambridge, MA, USA: MIT Press; 2002. p. 961–968.
   Google Scholar
• Pulido B, Gonzalez C. Possible conflicts: a compilation technique for consistency-based diagnosis. IEEE Trans. Syst. Man Cybern. Part B Cybern. 2004;34:2192–2206.10.1109/TSMCB.2004.835007
   PubMed Web of Science ®Google Scholar
• Bar-Shalom LCY. Control of discrete-time hybrid stochastic systems. IEEE Trans. Autom. Control. 1988;37:1522–1527.
   Google Scholar
• Pitt M, Shephard N. Filtering via simulation: auxiliary particle filter. J. Am. Stat. Assoc. 1999;94:590–599.10.1080/01621459.1999.10474153
   Web of Science ®Google Scholar
• Doucet A, Johansen AM. A tutorial on particle filtering and smoothing: fifteen years later. In: Crisan D, Rozovskiĭ B, editors. The Oxford handbook of nonlinear filtering. Oxford: Oxford University Press; 2011. p. 656–704.
   Google Scholar
• Murphy K. Dynamic Bayesian networks: representation, inference and learning [PhD thesis]. Berkeley (CA): Computer Science Division, UC Berkeley; 2002.
   Google Scholar
• Doucet A, de Freitas N, Gordon N, editors. Sequential Monte Carlo methods in practice. New York: Springer; 2001.
   Google Scholar
• Rubin D. Using the SIR algorithm to simulate posterior distributions. In: Bernardo JM, de Groot MH, Lindley DV, Smith AFM, editors. Bayesian Statistics 3. Oxford: Oxford University Press; 1988. p. 395–402.
   Google Scholar
• Doucet A, Godsill S, Andrieu C. On sequential monte carlo sampling methods for bayesian filtering. Stat. Comput. 2000;10:197–208.10.1023/A:1008935410038
   Web of Science ®Google Scholar
• Casella G, George E. Explaining the Gibbs sampler. Am. Stat. 1992;46:167–174.
   Web of Science ®Google Scholar
• Liu J. Metropolized independent sampling with comparisons to rejection sampling and importance sampling. Stat. Comput. 1996;6:113–119.10.1007/BF00162521
   Web of Science ®Google Scholar
• Kawahara Y, Fujimaki R, Yairi T, Machida K. Diagnosis method for spacecraft using dynamic Bayesian networks. In: Proceedings of The 8th International Symposium on Artifical Intelligence, Robotics and Automation in Space - iSAIRAS. 2005 Sep 5–8; Munich, Germany. ESA SP-603.
   Google Scholar
• Kawano I, Noguchi S, Kato H, Ueno H, Hattori H, Ikeda Y, Nagano Y. Study of crew cockpit simulator for Rendezvous docking and re-entry. Paper presented at: The 56^th Conference on Space Science and Technology. 2012 Nov 20–22; Bebbu, Oita, Japan.
   Google Scholar