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Evaluation of Sensor Failure Detection, Identification and Accommodation (SFDIA) Performance Following Common-Mode Failures of Pitot Tubes
Technical Paper
2014-01-2164
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Recent catastrophic air crashes have shown that physical redundancy is not a foolproof option for failures on Air Data Systems (ADS) on an aircraft providing airspeed measurements. Since all the redundant sensors are subjected to the same environmental conditions in flight, a failure on one sensor could occur on the other sensors under certain conditions such as extreme weather; this class of failure is known in the literature as “common mode” failure. In this paper, different approaches to the problem of detection, identification and accommodation of failures on the Air Data System (ADS) of an aircraft are evaluated. This task can be divided into component tasks of equal criticality as Sensor Failure Detection and Identification (SFDI) and Sensor Failure Accommodation (SFA). Data from flight test experiments conducted using the WVU YF-22 unmanned research aircraft are used. Analytical redundancy is provided through a least squares modeling based approach and an extended Kalman filter approach to handle the Sensor Failure Accommodation (SFA) task. From experiments, it is seen that both these approaches provide reasonable estimates of airspeed with an average estimation error of 0.533 m/s and standard deviation of 1.6446 m/s.
Furthermore two approaches to the task of Sensor Failure Detection and Identification (SFDI) based on different fault detection filters were evaluated. A Cumulative Sum (CUSUM) detector and the Generalized Likelihood Ratio Test (GLRT) detector were evaluated for different failure conditions - a sudden step bias, a fast rising fault and a slow rising fault in the measured airspeed and compared in terms of sensitivity to the failure magnitude, detection delay, false alarms and undetected faults. It was determined that on an average, the CUSUM filter performed slightly better in terms of detecting failures than the GLRT based detection for the given set of data.
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Authors
Citation
Gururajan, S., Fravolini, M., Rhudy, M., Moschitta, A. et al., "Evaluation of Sensor Failure Detection, Identification and Accommodation (SFDIA) Performance Following Common-Mode Failures of Pitot Tubes," SAE Technical Paper 2014-01-2164, 2014, https://doi.org/10.4271/2014-01-2164.Also In
References
- Belcastro , C. M. , and Jacobson , S. R. Future Integrated Systems Concept for Preventing Aircraft Loss-of-Control Accidents AIAA Guidance, Navigation, and Control Conference Toronto, Canada Aug. 2 5 2010
- Joint Planning and Development Office Concept of Operations for the Next Generation Air Transportation System http://www.jpdo.gov/library.asp Oct. 2009
- Williams-Hayes , P. S. Flight Test Implementation of a Second Generation Intelligent Flight Control System NASA Technical Report TM-2005-213669 2005
- Maine , T. , Burken , J. , Burcham , F. and Schaefer , P. Design challenges encountered in a propulsion controlled aircraft flight test program 30th AIAA/ASME/SAE/ASEE Joint Propulsion Conference Indianapolis, IN Jun. 27 29 1994
- Edwards , C. , Lombaerts , T. , and Smaili , H. Fault Tolerant Flight Control: a Benchmark Challenge Lecture Notes in Control and Information Sciences 399 10.1007/978-3-642-11690-2_1 2010
- Goupil , P. AIRBUS state of the Art and Practices on FDI and FTC in Flight Control System Control Engineering Practice 19 6 524 539 2011
- Perhinschi , M.G. , Campa , G. , Napolitano , M. R. , Lando , M. , Massotti , L. , Fravolini , M.L. Modelling and simulation of a fault tolerant flight control system International Journal of Modelling and Simulation 26 1 1 10 2006
- Perhinschi M.G. , Napolitano M.R. , Campa G. , Fravolini M.L. , Seanor B. Integration of Sensor and Actuator Failure Detection, Identification, and Accommodation Schemes Within Fault Tolerant Control Laws Control and Intelligent Systems 35 4 309 318 2007
- Napolitano , M.R. , An , Y. , and Seanor , B. A fault tolerant flight control system for sensor and actuator failures using neural networks Aircraft Design 3 2 103 128 2000
- Levine J. X-31's loss http://www.nasa.gov/centers/dryden/news/X-Press/stories/2004/013004/new_x31.html 2004
- McKenna J.T. Blocked Static Ports Eyed in Aeroperu 757 Crash Aviation Week and Space Technology 145 20 76 1996
- Belcastro C.M. , Foster J.V. Aircraft Loss-of-Control Accident Analysis 2010 AIAA Guidance, Navigation, and Control Conference Toronto, Canada Aug. 2 5 2010
- Campa G. , Fravolini M.L. , Seanor B. , Napolitano M.R. , Del Gobbo D. , Yu G. , Gururajan S. On-Line Learning Neural Networks for Sensor Validation for the Flight Control System of a B777 Research Scale Model International Journal of Robust and Nonlinear Control 12 11 987 1007 2002
- Frank P.M. Fault Diagnosis in Dynamic Systems Using Analytical and Knowledge-Based Redundancy: A Survey and Some New Results Automatica 26 3 459 474 1990
- Patton , R.J. Fault Detection and Diagnosis in Aerospace Systems Using Analytical Redundancy Computing & Control Engineering Journal 2 3 127 136 1991
- Hwang , I. , Kim , S. , Kim , Y. , and Seah , C.E. A Survey of Fault Detection, Isolation, and Reconfiguration Methods IEEE Transactions on Control System Technology 18 3 636 653 2010
- Moschitta A. , Fravolini M.L. , Carbone P. , Tissi F. Practical implementation issues in detecting voltage dips IEEE Instrumentation and Measurement Technology Conference (I2MTC) 159 164 2012
- Mammarella M. , Campa G. , Napolitano M. , Fravolini M.L. , Perhinschi M. , Gu Y. Machine Vision / GPS Integration Using EKF for the UAV Aerial Refueling Problem IEEE Transactions on Systems, Man, and Cyber and Cybernetics, Part C: Applications and Reviews 38 6 791 801 2008
- Fravolini M.L. , Campa G. Design of a Neural Network Adaptive controller via a constrained Invariant Ellipsoids Technique IEEE Transactions on Neural Networks 22 4 627 638 2011
- Fravolini M.L. , Campa G. , Napolitano M.R. Performance-Oriented Adaptive Neural Augmentation of an Existing Formation Flight Controller IET Control Theory & Applications Journal, IET Control Theory Appl. 2011 5 16 1819 1828
- Napolitano , M. R. 2005 Development of formation flight control algorithms using 3 YF-22 flying models Final report, Air Force Office of Scientific Research, AFOSR Grant Number F49620-01-1-0373
- Campa G. , Gu Y. , Seanor B. , Napolitano M. , Pollini L. , Fravolini M.L. Design And Flight Testing Of Nonlinear Formation Control Laws Control Engineering Practice 15 9 1077 1092 2007
- Basseville M. , Nikiforov I 1993 Detection of abrupt changes-theory and practice Prentice-Hall Englewood Cliffs, New York
- Farrell J.A. and Polycarpou M. M. Adaptive Approximation Based Control: Unifying Neural, Fuzzy and Traditional Adaptive Approximation Approaches New York Wiley 2006
- Wang H. , Wang Y. Neural-network-based fault-tolerant control of unknown nonlinear systems Control Theory and Applications, IEE Proceedings 146 5 389 398 1999
- Stevens B. , and Lewis F. Aircraft Control and Simulation John Wiley & Sons NY 1992
- Napolitano , M.R. Aircraft Dynamics: from Modeling to Simulation John Wiley and Sons Hoboken, NJ Nov. 2011
- Bureau d'Enquêtes et d'Analyses pour la sécurité de l'aviation civile Interim report no. 3: on the accident on 1 June 2009 to the Airbus A330-203 registered F-GZCP operated by Air France flight AF 447 Rio de Janeiro - Paris Report Jul. 29 2011
- Eubank , R.D. , Atkins , E.M. , and Ogura , S. Fault Detection and Fail-Safe Operation with a Multiple-Redundancy Air-Data System AIAA Guidance, Navigation, and Control Conference Toronto, Canada Aug. 2 5 2010
- Gururajan , S. , Fravolini , M.L. , Rhudy , M. , Chao , H. , Napolitano , M.R. Fault detection, classification and accommodation techniques for “Common Mode” failures on the airspeed sensor of an Unmanned Aerial Vehicle Fault Detection: Classification, Techniques and Role in Industrial Systems Nova Publishers Hauppauge, NY
- Fravolini , M.L. , Gururajan , S. , De Angelis , G. , Moschitta , A. , Chao , H. , Napolitano , M.R. UAV Analytical Redundancy based fault detection of the Airspeed Sensor via Generalized Likelihood Ratio Test Proceedings of the AIAA Guidance Navigation and Control Conference 19 22 August 2013 Boston, MA