Abstract
Particle filter (PF) is often used to estimate and predict the system state when the system model is known.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen M, Zhou D (2003) Fault prediction techniques for dynamic systems. Control Theory Appl 20(6):819–820
Cappe O, Godsill SJ, Moulines E (2007) An overview of existing methods and recent advances in sequential Monte Carlo. IEEE Proc 95(5):899–924
Wu JW, Trivedi MM (2007) Simultaneous eye tracking and blink detection with interactive particle filters. EURASIP J Adv Signal Proc 2008(1):1–17
Guo D, Wang X, Chen R (2005) New sequential Monte Carlo methods for nonlinear dynamic systems. Stat Comput 15(2):135–147
Kashiwaya S (2007) Chemical reaction rate parameter estimation by MAP particle filter algorithm. In: 2007 IEEE congress on evolutionary computation. pp 4489–4496
Changhua Hu, Zhang Qi, Qiao Y (2008) Strong tracking particle filter with application to fault prediction. Acta Automatica Sinica 34(12):1522–1528
Zhang Qi, Changhua Hu, Qiao Y (2008) Particle filter algorithm based on weight selected. Control Decis Mak 23(1):117–120
Zhang Qi, Xin W, Changhua H, et al. (2008) Research on artificial immune particle filter. Control Decis Mak 23(3):293–296+301
Zhang Qi, Changhua Hu, Qiao Y et al (2009) Fault prediction algorithm based on stochastic perturbed particle filter. Control Decis Mak 24(2):284–288
Zhang Qi, Changhua Hu, Qiao Y (2009) Fault prediction method based on clustering particle filter. Inf Control 38(1):115–120
Zhang Qi, Changhua Hu, Qiao Y et al (2009) Fault prediction algorithm according to particle filter algorithm based on weight optimization. Syst Eng Electron 31(1):221–224
Zhang Qi, Changhua Hu (2007) Study of particle filter with dynamic particle number. Control Eng 14(7):32–34
Li T (2003) Application of nonlinear filtering method in navigation system. National University of Defense Technology, Changsha
Doucet A, Godsill S (1998) On sequential Monte Carlo sampling methods for Bayesian filtering. University of Cambridge, Cambridge
Liu JS, Chen R (1998) Sequential Monte Carlo methods for dynamic systems. J Am Stat 83:1032–1044
Carpenter J, Clifford P, Fearnhead P (1999) Improved particle filter for nonlinear problems. IEE Proc-Radar, Sonar Navig 146(1):2–7
Arulampalam S, Maskell S, Gordon N (2002) A tutorial on particle filters for online non-Gaussian Bayesian tracking. IEEE Trans Signal Proc 50(2):174–188
Gordon NJ, Salmond DJ, Smith AFM (1993) Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proc-F 140(2):107–113
Berzuini C, Best NG, Gilks WR et al (1997) Dynamic conditional independence models and Markov chain Monte Carlo methods. J Am Stat Assoc 92:1403–1411
Crisan D, Doucet A (2002) A survey of convergence results on particle filtering methods for practitioners. IEEE Trans Signal Proc 50(3):736–746
Kunsch HR (2001) State space and hidden Markov models. In: Barndorff-Nielsen OE, Cox DR, Kluppelberg C (eds) Complex stochastic systems. CRC press, London, pp 109–173
Yang X (2006) A study of hybrid estimation theory and applications based on particle filtering. Northwestern Polytechnical University, Xi’an
Mo Y, Xiao D (2005) Evolutionary particle filter and its application. Control Theory Appl 22(2):269–270
Doucet A, Godsill S, Andrieu C (2000) On sequential Monte Carlo sampling methods for Bayesian filtering. Stat Comput 10(1):197–208
Xu ZG, Ji YD, Zhou DH (2008) Real-time reliability prediction for a dynamic system based on the hidden degradation process identification. IEEE Trans Reliab 57(2):230–242
Doucet A, Tadić VB (2003) Parameter estimation in general state-space models using particle methods. Ann Inst Stat Math 55:409–422
Chen MZ, Zhou DH (2001) Particle filtering based fault prediction of nonlinear systems. In: IFAC symposium proceedings of safe process. Elsevier Science, Washington, pp 2971–2977
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2022 National Defense Industry Press
About this chapter
Cite this chapter
Hu, C., Fan, H., Wang, Z. (2022). Weight Optimization-Based Particle Filter Algorithm for Degradation Modeling and Residual Life Prediction. In: Residual Life Prediction and Optimal Maintenance Decision for a Piece of Equipment. Springer, Singapore. https://doi.org/10.1007/978-981-16-2267-0_9
Download citation
DOI: https://doi.org/10.1007/978-981-16-2267-0_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-2266-3
Online ISBN: 978-981-16-2267-0
eBook Packages: EngineeringEngineering (R0)