An SFDI observer-based scheme for a general aviation aircraft

Marco Ariola; Massimiliano Mattei; Immacolata Notaro; Federico Corraro; Adolfo Sollazzo

International Journal of Applied Mathematics and Computer Science (2015)

  • Volume: 25, Issue: 1, page 149-158
  • ISSN: 1641-876X

Abstract

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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.

How to cite

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Marco Ariola, et al. "An SFDI observer-based scheme for a general aviation aircraft." International Journal of Applied Mathematics and Computer Science 25.1 (2015): 149-158. <http://eudml.org/doc/270390>.

@article{MarcoAriola2015,
abstract = {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.},
author = {Marco Ariola, Massimiliano Mattei, Immacolata Notaro, Federico Corraro, Adolfo Sollazzo},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {model-based FDI; sensor FDI; extended Kalman filtering; analytical redundancy; flight control},
language = {eng},
number = {1},
pages = {149-158},
title = {An SFDI observer-based scheme for a general aviation aircraft},
url = {http://eudml.org/doc/270390},
volume = {25},
year = {2015},
}

TY - JOUR
AU - Marco Ariola
AU - Massimiliano Mattei
AU - Immacolata Notaro
AU - Federico Corraro
AU - Adolfo Sollazzo
TI - An SFDI observer-based scheme for a general aviation aircraft
JO - International Journal of Applied Mathematics and Computer Science
PY - 2015
VL - 25
IS - 1
SP - 149
EP - 158
AB - 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.
LA - eng
KW - model-based FDI; sensor FDI; extended Kalman filtering; analytical redundancy; flight control
UR - http://eudml.org/doc/270390
ER -

References

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  17. 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. 
  18. Yeh, Y.C. (1996). Triple-triple redundant 777 primary flight computer, Proceedings of the Aerospace Applications Conference, Aspen, CO, USA, pp. 293-307. 

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