UAV Sensor Fault Diagnosis Technology: A Survey

Article Preview

Abstract:

In this paper,a survey of UAV (unmanned aerial vehicle ) sensor fault diagnosis technology is presented .UAV sensor fault modes and feature are analyzed. The current research progress and significant approaches in the field are introduced at home and abroad. Finally, the difficulties to be solved and future development trends in the research field are summarized.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

1833-1837

Citation:

Online since:

November 2012

Keywords:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2012 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Office of the Secretary of Defense. Unmanned Aerial Roadmap 2002-2027 [R]. Washington, DC 20301: Office of the Secretary of Defense, 2002.

Google Scholar

[2] HEREDIA G, OLLERO A, MAHTANI R, BEIJAR M. EMUSS V, MUSIAL M. Detection of sensor faults in autonomous helicopters[A]. Proc of the International Conference on Robotics and Automation [C]. Barcelona, Spain, 2005.

DOI: 10.1109/robot.2005.1570444

Google Scholar

[3] Zhang Hailin, Yang Ping. A Research on Fault Detection Diagnosis Technology for Sensor, Mechatronics, 2001, (5):73-78

Google Scholar

[4] QI Jun-tong,HAN Jian-da. Fault diagnosis and fault-tolerant control of rotorcraft flying robots: a survey [J]. CAAI Transactions on Intelligent Systems,2007,2(2):31-39.

Google Scholar

[5] Sullivan J., McCarty P., Yoshimoto G.,etc.. Intelligent system for autonomous UUV monitoring, diagnosis, and control, Proceedings of 1992 AIAA Guidance, Navigation, and Control Conference, 1992.

Google Scholar

[6] Rong Jili.Study on Intelligent Technique of Onboard Fault Diagnosis for Spacecrafts [D]. Harbin Institute of Technology doctoral dissertation, 1996.

Google Scholar

[7] M. Oosterom, R. Babuska, and H. B. Verbruggen, "Soft computing applications in aircraft sensor management and flight control law reconfiguration," Systems, Man and Cybernetics, Part C, IEEE Transactions on, vol. 32, pp.125-139, 2002.

DOI: 10.1109/tsmcc.2002.801357

Google Scholar

[8] A. Ichtev, J. Hellendoorn, R. Babuska. "Fault detection and isolation using multiple Takagi- Sugeno fuzzy models," presented at Fuzzy Systems, 2001. The 10th IEEE International Conference on, 2001.

DOI: 10.1109/fuzz.2001.1008946

Google Scholar

[9] Liu Yiping. An Fault Classifier Based on Neuro-Fuzzy Networks with Extension to Multi-sensor Fault Diagnosis. Journal of Transduction Technology ,2000,19(1):38-43.

Google Scholar

[10] M. Oosterom and R. Babuska, "Virtual sensor for fault detection and isolation in flight control systems - fuzzy modeling approach," presented at Decision and Control, 2000. Proceedings of the 39th IEEE Conference on, 2000.

DOI: 10.1109/cdc.2000.914204

Google Scholar

[11] Yu Yong,Wan Dejun. Simulation Research on an Approach of Fault Detection Using Wavelet Neural Networks [J]. COMPUTER SIMULATION,2000,17(1).

Google Scholar

[12] SEUNGKEUN K,YOUDAN K,CHANGOOK P,INSUNG J.Hybird fault detection and isolation techniques for aircraft inertial measurement sensors[A].AIAA Guidance, Navigation, and Control Conference and Exhibit[C].Providence,2004.

DOI: 10.2514/6.2004-5419

Google Scholar

[13] Rago C., Prasanth R., Mehra R., Fortenbaugh R., Failure detection and identification and fault tolerant control using the IMM-KF with applications to the Eagle-Eye UAV, Proceedings of the 37th IEEE Conference on Decision ad Control, Tampa, Florida, (1998)

DOI: 10.1109/cdc.1998.761963

Google Scholar

[14] Heredia G., Ollero A. et al. Detection of sensor faults in autonomous helicopters, Proc. of the 2005 IEEE International Conference on Robotics and Automation (ICRA 2005), Barcelona, Spain, Vol. 18-22(2005),p.2229

DOI: 10.1109/robot.2005.1570444

Google Scholar

[15] M.Demetriou.Robust Adaptive Techniques for Sensor Fault Detection and Diagnosis. In Proceedings of the 37th IEEE Conference on Decision & Control [C].Tampa,florida USA.December 1998.

DOI: 10.1109/cdc.1998.760852

Google Scholar

[16] Cork L., Walker R., Dunn S. Fault detection identification and accommodation techniques for Unmanned Airborne Vehicles, In Proceedings Australian International Aerospace Congress, Melbourne, (2005)

Google Scholar

[17] Giampiero Campa, Mario Luca Fravolini, Marcello Naoplitano. Neural Networks-Based Sensor Validation for the Flight Control System of a B777 Research Model. proceedings of the American Control Conference. Anchorage,AK,vol.1(2002), p.412.

DOI: 10.1109/acc.2002.1024840

Google Scholar

[18] M. R. Napolitano, Y. An, and B. A. Seanor, "A fault tolerant flight control system for sensor and actuator failures using neural networks", Aircraft Design, vol. 3, pp.103-128, 2000.

DOI: 10.1016/s1369-8869(00)00009-4

Google Scholar

[19] Tom Brotherton, Paul Grabill .A Tested for Data Fusion for Helicopter Diagnostics and Prognostics. Proceedings of the 2003 IEEE Aerospace Conference, March 2003.

DOI: 10.1109/aero.2003.1234178

Google Scholar

[20] GU Wei,HUANG Zhi-yi,ZHANG Wei-guo etc. A Method of Fault Identification Using BP Neural Network for Flight Control Systems.COMPUTER SIMULATION, 2011, 28 (5) :52-55

Google Scholar

[21] HU Liang-mou CAO Ke-qiang SU Xin-bing LI Xiao-gang. Fault diagnosis for flight control system's sensor based on LS-SVM [J]. FLIGHT DYNAMICS ,2011,29(3):36-39,43

Google Scholar

[22] WU Kang; HAN Bo; LI Ping. Fault detection and isolation for sensors on SUH based on SVR and wavelet transform. Computer Engineering and Applications, 2011, 47(20):221-224

Google Scholar

[23] NING Dong-fang,ZHANG Wei-guo, LI Bin. Fault Diagnosis for Flight Control System Based on Genetic Wavelet Network. COMPUTER SIMULATION, 25(3), 2008,:83-86.

Google Scholar