Sensor fault detection and accommodation using neural networks with application to a non-linear unmanned air vehicle model

Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering

Volumn 224, Issue 4, 2010, Pages 437-447

  Samy, I ^a   Postlethwaite, I ^a   Gu, D W ^a  

^a UNIVERSITY OF LEICESTER   (United Kingdom)

Author keywords

Neural networks; Residual generator; Sensor fault detection and accommodation; Unmanned air vehicles

Indexed keywords

CONVENTIONAL APPROACH; MODEL-BASED FAULT DETECTION; NON-LINEAR; NUMBER OF FALSE ALARMS; ONLINE LEARNING; RADIAL BASIS FUNCTIONS; RESIDUAL GENERATION; RESIDUAL GENERATOR; RESOURCE ALLOCATION NETWORKS; SENSOR FAULT DETECTION; SINGLE SENSOR; UNMANNED AIR VEHICLES;

AEROSPACE ENGINEERING; NEURAL NETWORKS; OBSERVABILITY; RADIAL BASIS FUNCTION NETWORKS; REMOTELY OPERATED VEHICLES; SENSORS; UNMANNED AERIAL VEHICLES (UAV); VEHICLES;

FAULT DETECTION;

EID: 77950612949     PISSN: 09544100     EISSN: None     Source Type: Journal    
DOI: 10.1243/09544100JAERO579     Document Type: Article

References (18)

1. An, Y. A design of fault tolerant flight control systems for sensor and actuator failures using on-line learning neural networks. PhD Thesis, Department of Mechanical and Aerospace Engineering,West Virginia, USA, 1998.
2. Chen, J. and Patton, R. J. Robust model-based fault diagnosis for dynamic systems, 1999 (Kluwer Academic, USA).
3. Simani, S., Fantuzzi, C., and Patton, R. J. Model-based fault diagnosis in dynamic systems using identification techniques, 2003 (Springer-Verlag, London).
4. Isermann, R. and Balle, P. Trends in the application of model-based fault detection and diagnosis of technical processes. Control Engng Pract., 1997, 5, 709-719. (Pubitemid 27414363)
5. Campa, G., Fravolini, M. L.,Napolitano, M., and Seanor, B. Neural networks based sensor validation for the flight control system of a B777 research model. Am. Control Conf., 2002, 1, 412-417.
6. Samy, I., Postlethwaite, I., and Gu,D.W. Neural network sensor validation scheme demonstrated on aUAVmodel. In Proceedings of the IEEE Conference on Decision and Control,Mexico, 2008, pp. 1237-1242.
7. Samy, I., Postlethwaite, I., and Gu, D. W. Detection of additive sensor faults in a UAV model using neural networks. In Proceedings of the UKACC Conference, Manchester, UK, 2-4 September 2008.
8. Heredia, G.,Ollero, A.,Mahtani,R.,Bejar,M.,Remuss,V., andMusial,M.Detection of sensor faults in autonomous helicopters. In Proceedings of the IEEE International Conference on Robotics and Automation, Barcelona, Spain, 2005, pp. 2229-2234. (Pubitemid 46084119)
9. Capriglione, D., Liguori, C., and Pietrosanta, A. Real time implementation of IFDIA scheme in automotive systems. IEEE Trans. Instrum.Meas., 2007, 56, 824-830. (Pubitemid 46784551)
10. Franchi, P. L. Grand designs: an EC-funded research project has unveiled its proposals for a new generation of aircraft that are intended to give Europe the edge in the civil UAV sector. Flight Int., 2005, 109-114.
11. Chow, E. Y. and Willsky, A. S. Analytical redundancy and the design of robust detection systems. IEEE Trans. Automat. Control, 1984, 29, 603-614. (Pubitemid 14608327)
12. Patton, R. J., Frank, P. M., and Clark, R. N. Fault diagnosis in dynamic systems, theory and application, 1989 (Control Engineering Series, Prentice Hall, London).
13. Emami-Naeini, A. E., Akhter, M. M., and Rock, S. M. Effect of model uncertainty on failure detection: the threshold selector. IEEE Trans. Automat. Control, 1988, 33, 1106-1115.
14. Mehra, R. K. and Peschon, J. An innovations approach to fault detection and diagnosis in dynamic systems. Automatica, 1971, 7, 637-640.
15. Watanabe, K. and Himmelblau, D. M. Instrument fault detection in systems with uncertainties. Int. J. Syst. Sci., 1982, 13, 137-158. (Pubitemid 12551260)
16. Li,Y., Sundararajan, N., and Saratchandran, P. Analysis of minimal radial basis function network algorithm for real time identification of nonlinear dynamic systems. IEEE Control Theory Appl., 2000, 4, 476-484.
17. Buonanno, A. An aerodynamic simulation model of the Eclipse UAV, FLAVIIR project internal report, FLAV-A01- 002, 2005.
18. Fravolini, M. L.,Campa, G.,Napolitano, M., and Song,Y. Minimal resource allocating networks for aircraft SFDIA. Adv. Intell.Mechatronics, 2001, 2, 1251-1256.