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2015 | 25 | 1 | 149-158
Tytuł artykułu

An SFDI observer-based scheme for a general aviation aircraft

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The problem of detecting and isolating sensor faults (sensor fault detection and isolation-SFDI) on a general aviation aircraft, in the presence of external disturbances, is considered. The proposed approach consists of an extended Kalman observer applied to an augmented aircraft plant, where some integrators are added to the output variables subject to faults. The output of the integrators should be ideally zero in the absence of model uncertainties, external disturbances and sensor faults. A threshold-based decision making system is adopted where the residuals are weighted with gains coming from the solution to an optimization problem. The proposed nonlinear observer was tested both numerically on a large database of simulations in the presence of disturbances and model uncertainties and on input-output data recorded during real flights. In this case, the possibility of successfully applying the proposed technique to detect and isolate faults on inertial and air data sensors, modelled as step or ramp signals artificially added to the real measurements, is shown.
Rocznik
Tom
25
Numer
1
Strony
149-158
Opis fizyczny
Daty
wydano
2015
otrzymano
2014-02-03
poprawiono
2014-07-30
Twórcy
autor
  • Department of Engineering, University of Naples Parthenope, Isola C4, 80143 Naples, Italy
  • Department of Industrial and Information Engineering (DIII), Second University of Naples, Real Casa dell'Annunziata Via Roma, 29, Aversa (CE), 81031, Italy
  • Department of Industrial and Information Engineering (DIII), Second University of Naples, Real Casa dell'Annunziata Via Roma, 29, Aversa (CE), 81031, Italy
  • Guidance, Navigation and Control Laboratory, Italian Aerospace Research Center (CIRA), Capua (CE), Italy
  • Guidance, Navigation and Control Laboratory, Italian Aerospace Research Center (CIRA), Capua (CE), Italy
Bibliografia
  • Basseville, M. and Nikiforov, I.V. (1993). Detection of Abrupt Changes: Theory and Application, Prentice Hall, Englewood Cliffs, NJ.
  • Chen, J. and Patton, R.J. (1999). Robust Model-Based Fault Diagnosis for Dynamic Systems, Kluwer Academic Publishers, Norwell, MA.
  • Clark, R.N. (1978). A simplified instrument failure detection scheme, IEEE Transactions on Aerospace and Electronic Systems 14(4): 558-563.
  • Clark, R.N., Fosth, D.C. and Walton, V.M. (1975). Detecting instrument malfunctions in control systems, IEEE Transactions on Aerospace and Electronic Systems 11(4): 465-473.
  • Gertler, J. (1997). Fault detection and isolation using parity relations, Control Engineering Practice 5(5): 653-661.
  • Gertler, J. (1998). Fault Detection and Diagnosis in Engineering Systems, Marcel Dekker, New York, NY.
  • Gustafsson, F. (2000). Adaptive Filtering and Change Detection, Wiley, New York, NY.
  • Isermann, R. (1984). Process fault detection based on modeling and estimation methods: A survey, Automatica 20(4): 387-404.
  • Kalman, R.E. (1960). A new approach to linear filtering and prediction problems, Journal of Basic Engineering 82(1): 35-45.
  • Luenberger, D.G. (1971). An introduction to observers, IEEE Transactions on Automatic Control 16(6): 596-602.
  • Mattei, M., Paviglianiti, G. and Scordamaglia, V. (2005). Nonlinear observers with H-infinity performance for sensor fault detection and isolation: A linear matrix inequality design procedure, Control Engineering Practice 13(10): 1271-1281.
  • Mehra, R.K. and Peschon, J. (1971). An innovations approach to fault detection and diagnosis in dynamic systems, Automatica 7(5): 637-640.
  • Patton, R.J. and Chen, J. (1994). A review of parity space approaches to fault diagnosis for aerospace systems, Journal of Guidance, Control, and Dynamics 17(2): 278-285.
  • Simani, S., Fantuzzi, C. and Patton, R.J. (2003). Model-based Fault Diagnosis in Dynamic Systems Using Identification Techniques, Springer-Verlag, New York, NY.
  • Stevens, B.L. and Lewis, F.L. (2003). Aircraft Control and Simulation, 2nd Edn., John Wiley & Sons, Inc., Hoboken, NJ.
  • Watanabe, K. and Himmelblau, D.M. (1982). Instrument fault detection in system with uncertainties, International Journal of Systems Science 13(2): 137-158.
  • Wüunnenberg, J. and Frank, P.M. (1987). Sensor fault detection via robust observers, in S. Tzafestas, M. Singh and G. Schmidt (Eds.), System Fault Diagnostics, Reliability & Related Knowledge-based Approaches, Vol. 1, D. Reidel Publishing Company, Dordrecht, pp. 147-160.
  • Yeh, Y.C. (1996). Triple-triple redundant 777 primary flight computer, Proceedings of the Aerospace Applications Conference, Aspen, CO, USA, pp. 293-307.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv25i1p149bwm
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