PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

A robust fixed-lag smoothing algorithm for dynamic systems with correlated sensor malfunctions

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A new robust fixed-lag smoothing algorithm for fault-tolerant signal processing in stochastic dynamic systems in the presence of correlated sensor malfunctions has been developed. The algorithm is developed using a state vector augmentation method and the Gaussian approximation of the current estimate probability density function. The algorithm can be used in the real-time fault-tolerant control systems as well as in radar tracking systems working in the complex interference environment. The performance of the developed algorithm are evaluated by simulations and compared with smoothing and nonlinear filtering algorithms.
Rocznik
Strony
517--523
Opis fizyczny
Bibliogr. 21 poz., rys., wykr.
Twórcy
  • Faculty of Electrical Engineering, Bialystok Technical University, 45D Wiejska St., 15-351 Bialystok, Poland
autor
  • Faculty of Electrical Engineering, Bialystok Technical University, 45D Wiejska St., 15-351 Bialystok, Poland, djanczak@pb.edu.pl
Bibliografia
  • A robust fixed-lag smoothing algorithm for dynamic systems with correlated sensor malfunctions
  • [1] J.J. Gertler, Fault Detection and Diagnosis in Engineering Systems, Marcel Dekker, NewYork, 1998.
  • [2] J. Korbicz, J.M. Koscielny, and Z. Kowalczuk, Fault Diagnosis: Models, Artificial Intelligence, Applications, Springer Verlag, Berlin, 2004.
  • [3] R.J. Patton, P.M. Frank, and R.N. Clark, Issues of Fault Diagnosis for Dynamic Systems, Springer-Verlag, Berlin, 2000.
  • [4] Tao Wei, Yufei Huang, and C.L.P. Chen, “Adaptive sensor fault detection and identification using particle filter algorithms”, IEEE Trans. on Systems, Man, and Cybernetics 39, 201–213 (2009).
  • [5] K. Khemiri, F. Ben Hmida, J. Ragot, and M. Gossa, “Novel optimal recursive filter for state and fault estimation of linear stochastic systems with unknown disturbances”, Int. J. Applied Mathematics and Computer Science 21 (4), 629–638 (2011).
  • [6] S. Wolinski, “Defining of the structural robustness”, Bull. Pol. Ac.: Tech. 61 (1), 137–144 (2013).
  • [7] Yu.P. Grishin and D. Janczak, “State estimation of linear dynamic system with unknown input and uncertain observation with using dynamic programming”, Control and Cybernetics 35 (4), 851–862 (2006).
  • [8] Yu.P. Grishin and D. Janczak, “A detection-estimation method for systems with random jumps with application to target tracking and fault diagnosis”, Nonlinear Dynamics, INTEH 1, 343–366 (2010).
  • [9] D.G. Lainiotis and S.K. Park, “Joint detection, estimation and system identification: discrete data case”, Int. J. Control 17 (3), 609–633 (1973).
  • [10] A.P. Sage and J.L. Melse, Estimation Theory with Application to Communication and Control, McGraw-Hill, New York, 1972.
  • [11] H. Dong, Z. Wang, and H. Gao, ‘Fault detection for Markovian jump systems with sensor saturations and randomly varying nonlinearities”, IEEE Trans. on Circuits and Systems 59 (10), 2354–2362 (2012).
  • [12] A.S. Willsky and H.L. Jones, “A generalized likelihood ratio approach to detection and estimation of jumps in linear systems”, IEEE Trans. on Automatic Control 21 (1), 108–112 (1976).
  • [13] J. Klamka, A. Czornik, and M. Niezabitowski, “Stability and controllability of switched systems”, Bull. Pol. Ac.: Tech. 61 (3), 547–555 (2013).
  • [14] E. Balaban, A. Saxena, P. Bansal, K.F. Goebel, and S. Curran, “Modeling, detection, and disambiguation of sensor faults for aerospace applications”, IEEE Sensors J. 9 (12), 1907–1917 (2009).
  • [15] S. Rogers, “Sensor noise fault detection”, Proc. 2003 American Control Conf. 5, 4267–4268 (2003).
  • [16] Xi. Zhang, “Sensor bias fault detection and isolation in a class of nonlinear uncertain systems using adaptive estimation”, IEEE Trans. on Automatic Control 56 (5), 1220–1226 (2011).
  • [17] Y. Bar-Shalom, P.K. Willet, and X. Tian, Tracking and Data Fusion, YBS Publishing, London, 2011.
  • [18] B. Ristic, S. Arulampalam, and N. Gordon, Beyond the Kalman Filter-Particle Filters for Tracking Applications, Artech House, New York, 2004.
  • [19] M. Mrugalski, “An unscented Kalman filter in designing dynamic GMDH neural networks for robust fault detection”, Int. J. Applied Mathematics and Computer Science 23 (1), 157–170 (2013).
  • [20] M. Mosallae and K. Salahshoor, “Sensor fault detection using adaptive modified extended Kalman filter based on data fusion technique”, Proc. 4th Int. Conf. on Information and Automation for Sustainability 1, 513–518 (2008).
  • [21] T. Katayma and S. Sugimoto, (ed), Statistical Methods in Control and Signal Processing, Marcel Dekker, New York, 1997.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-02e9f2e0-5af0-4b8b-a73d-ea9589a8abdd
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.