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Fault Diagnosis Method Integrated Fuzzy Logic and Particle Filter for Nonlinear Systems

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PL
Algorytm wykrywania awarii w systemach nieliniowych - wykorzystanie logiki rozmytej i filtru cząsteczkowego
Języki publikacji
EN
Abstrakty
EN
A new fault diagnosis method based on integrated fuzzy logic and particle filter for nonlinear systems is proposed to improve the accuracy of fault diagnosis. The Water Level and Temperature Control System is taken as test-bed process, with different switching states simulating possible system faults. The simulation result show that the proposed method could diagnose fault more accurately than that based on two-valued logic.
PL
W artykule zaprezentowano nową metodę wykrywania awarii w opartą na logice rozmytej i filtrze cząsteczkowym. Metoda dedykowana układom nieliniowym, zwiększa dokładność detekcji stanów niepożądanych. Przeprowadzone badania symulacyjne i eksperymentalne w układzie regulacji temperatury oraz poziomu cieczy, potwierdziły zwiększoną skuteczność algorytmu dla różnych przypadków awarii.
Rocznik
Strony
283--287
Opis fizyczny
Bibliogr. 20 poz., tab., rys.
Twórcy
autor
autor
Bibliografia
  • [1] D.R.Espinoza-Trejo,D.U.Campos-Delgado. Detection and isolation of actuator faults for a class of non-linear systems with application to electric motors drives, IET Control Theory and Applications. , 3(2009), No.10, pp.1317-1329.
  • [2] Mingxing Jia, Xiaoping Guo, Chunhui Zhao, Dong Xiao. Nonlinear Fault Diagnosis based on RBF with Sliding Window Error Feedback, Proc. Int. Conf. on Computational Engineering in Systems applications, Beijing, China, October (2006), pp. 1980-1983.
  • [3] H. Xue, J.G. Jiang. Fault Detection and Accommodation for Nonlinear Systems Using Fuzzy Neural Networks, Proc. 5th Int. Conf. Power Electronics and Motion Control, (2006), pp.1-5.
  • [4] Frank P.M. Fault Diagnosis in Dynamic Systems Using Analytical and Knowledge-based Redundancy—A Survey and Some New Results, Automatica, 26(1990),no.3: 459-474.
  • [5] B.D.Anderson, J.B.Moore. Optimal Filtering, Englewood Cliffs, NJ: Prentice-Hall, (1979).
  • [6] M.Sanjeev Arulampalam, Simon Maskell, Neil Gordon, Tim Clapp. A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking, IEEE TRANSACTION ON SIGNAL PROCESSING, 50(2002), no.2, pp. 174-188.
  • [7] Petar M.Djuric, Jayesh H.Kotecha, Jianqui Zhang, Rufei Huang, Tadesse Ghirmai, Monica F.Bugallo, Joaquin Miguez. Particle Filtering, IEEE SIGNAL PROCESSING MAGZINE, September, pp.19-38. (2003)
  • [8] Fredrik Gustafsson, Fredrik Gunnarsson, Niclas Bergman, Urban Forssell, Jonas Jansson, Rickard Karlsson, Per-Johan Nordlund. Particles Filters for Positioning, Navigation, and Tracking, IEEE TRANSACTIONS ON SIGNAL PROCESSING, 50(2002),no.2, pp. 425-437.
  • [9] V.Kadirkamanathan, P.Li, M.H.Jaward, S.G.Fabri. A sequential Monte Carlo filtering approach to fault detection and isolation in nonlinear systems, Proceedings of the 39th IEEE Conference on Decision and Control, Sydney, Australia, December (2000), pp.245-250.
  • [10] V.Kadirkamanathan, P.Li, M.H.Jaward, S.G.Fabri. Particle filtering-based fault detection in non-linear stochastic systems, International Journal of Systems Science, 33(2002),no.4, pp.259-265.
  • [11] LIANG J, QIAO L Y, PENG X Y. Fault Detection Based on SIR State Estimation and Smoothed Residual(in Chinese), ACTA ELECTRONICA SNICA, 35(2007),no.12, pp.32-36.
  • [12] LIANG J, PENG X Y. A particle filter with the resampling based on the similarity of observation path for single station passive target tracking with bearing-only measurement’ (in Chinese), Journal of Electronic Measurement and Instrument. 23(2009),no.2, pp.10-14.
  • [13] Gordon N, Salmond D. Novel approach to nonlinear and non- Gaussian Bayesian state estimation, Proc. Institute Electric Engineering, 140(1993),no.2, pp.107:113.
  • [14] James V.Candy. Bootstrap Particle Filtering, IEEE SIGNAL PROCESSING MAGAZINE, 24(2007),no.4, pp.73-85.
  • [15] L.A.Zadeh. Fuzzy Logic, Information Control, 8(1965),no.3, pp.338-353.
  • [16] Christopher J. White , Heba Lakany. A fuzzy inference system for fault detection and isolation: Application to a fluid system. Expert Systems with Applications. 35(2008),no.3,pp.1021- 1033.
  • [17] Mendonca.L.F, Sousa.J.M, Sa da Costa, J.M.G. Fuzzy modelbased fault detection and isolation, Proceedings of IEEE Conference on Emerging Technologies and Factory Automation, 2, pp.768-774. (2003).
  • [18] T.Kailath, A.H.Sayed, B.Hassibi. Linear estimation, in Information and System Sciences. Upper Saddle River, NJ: Prentice-Hall, (2000).
  • [19] Peng Wang, Xiao Ming Chen, Tunc Aldemir. DSD: a generic software for model-based fault diagnosis in dynamic systems, Reliability Engineering and System Safety. 75(2002), pp.31-39.
  • [20] Marzio Marseguerra, Enrico Zio. Monte Carlo simulation for model-based fault diagnosis in dynamic systems, Reliability Engineering and System Safety. 94(2009), pp.180-186.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPS4-0004-0103
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