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Structured non-self approach for aircraft failure identification within a fault tolerance architecture

Published online by Cambridge University Press:  23 March 2016

H. Moncayo ,
I. Moguel ,
M.G. Perhinschi ,
A. Perez ,
D. Al Azzawi  and
A. Togayev
H. Moncayo*
Affiliation:
Department of Aerospace Engineering, Embry-Riddle Aeronautical University, Daytona Beach, Florida, US
I. Moguel
Affiliation:
Department of Aerospace Engineering, Embry-Riddle Aeronautical University, Daytona Beach, Florida, US
M.G. Perhinschi
Affiliation:
Department of Mechanical and Aerospace Engineering, West Virginia University, Morgantown, West Virginia, US
A. Perez
Affiliation:
Department of Aerospace Engineering, Embry-Riddle Aeronautical University, Daytona Beach, Florida, US
D. Al Azzawi
Affiliation:
Department of Mechanical and Aerospace Engineering, West Virginia University, Morgantown, West Virginia, US
A. Togayev
Affiliation:
Department of Mechanical and Aerospace Engineering, West Virginia University, Morgantown, West Virginia, US
*
moncayoh@erau.edu
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Abstract

Within an immunity-based architecture for aircraft fault detection, identification and evaluation, a structured, non-self approach has been designed and implemented to classify and quantify the type and severity of different aircraft actuators, sensors, structural components and engine failures. The methodology relies on a hierarchical multi-self strategy with heuristic selection of sub-selves and formulation of a mapping logic algorithm, in which specific detectors of specific selves are mapped against failures based on their capability to selectively capture the dynamic fingerprint of abnormal conditions in all their aspects. Immune negative and positive selection mechanisms have been used within the process. Data from a motion-based six-degrees-of-freedom flight simulator were used to evaluate the performance in terms of percentage identification rates for a set of 2D non-self projections under several upset conditions.

Type
Research Article
Information
The Aeronautical Journal , Volume 120 , Issue 1225 , March 2016 , pp. 415 - 434
Copyright
Copyright © Royal Aeronautical Society 2016 

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References

REFERENCES

1.Dasgupta, D. (Ed). Artificial Immune Systems and Their Applications, 1999, Springer-Verlag, New York, New York, US.Google Scholar
2.Dasgupta, D. and Nino, L.F.Immunological Computation – Theory and Applications, 2009, CRC Press, Auerbach Publications, Taylor & Francis Group, Boca Raton, Florida, US.Google Scholar
3.Dasgupta, D. and Nino, F. Comparison of negative and positive selection algorithms in novel pattern detection, Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 2000, Nashville, Tennessee, US, pp 125–130.Google Scholar
4.De Castro, L. and Timmis, J.Artificial immune systems: A novel paradigm to pattern recognition, Artificial Neural Networks in Pattern Recognition, in Corchado, J. M., Alonso, L., Fyfe, C. (Eds), SOCO-2002, 2002, University of Paisley, UK, pp 67–84.Google Scholar
5.Lau, H.Y.K. and Ko, A. An immune robotic system for humanitarian search and rescue, Proceedings of ICARIS 2007, Lecture Notes in Computer Science, 2007, Springer, Santos.Google Scholar
6.Lee, J., Roh, M., Lee, J. and Lee, D. Clonal selection algorithms for 6-DOF PID control of autonomous underwater vehicles, Proceedings of ICARIS 2007, Lecture Notes in Computer Science, 2007, Vol 4628, Springer-Verlag Berlin Heidelberg, pp 182–190.Google Scholar
7.Hofmeyr, S.A., Somayaji, A. and Forrest, S.Intrusion detection using sequences of system calls, J Computer Security, 1998, 6, pp 151–180.Google Scholar
8.Twycross, J. and Aickelin, U. Libtissue-implementing innate immunity, Proceedings of IEEE World Congress on Computational Intelligence, 2006, Vancouver, Canada.Google Scholar
9.Serapiao, A.B.S., Ricardo, J., Mendes, P. and Miura, K. Artificial immune systems for classification of petroleum well drilling operations, Proceedings of the 6th International Conference on Artificial Immune Systems (ICARIS), 2007, Lecture Notes in Computer Science, 2007, Vol 4628, Springer-Verlag Berlin Heidelberg, pp 47–58.Google Scholar
10.Timmis, J. and Knight, T.Artificial immune systems: Using the immune system as inspiration for data mining, in Abbass, H.A., Sarker, R.A., Newton, C.S. (Eds), Data Mining: A Heuristic Approach, 2001, Idea Group Publishing, Hershey, Pennsylvania, US, pp 209–230.Google Scholar
11.Karr, C., Nishita, K. and Graham, K.Adaptive aircraft flight control simulation based on an artificial immune system, Applied Intelligence, 2005, 23, (3), pp 295–308.Google Scholar
12.Moncayo, H., Perhinschi, M.G., Wilburn, B., Wilburn, J. and Karas, O. UAV adaptive control laws using non-linear dynamic inversion augmented with an immunity-based mechanism, Proceedings of the AIAA Guidance, Navigation, and Control Conference, 2012, Minneapolis, Minnesota, US.Google Scholar
13.Takahashi, K. and Yamada, T.Application of an immune feedback mechanism to control systems, The Japan Soc Mechanical Engineers, JSME Int J, Series C, 1998, 41, (2), pp 184–191.Google Scholar
14.Gonzalez, F., Dasgupta, D. and Kozma, R. Combining negative selection and classification techniques for anomaly detection, Proceedings of the 2002 Congress on Evolutionary Computation CEC2002, IEEE Press, 2002, Honolulu, Hawaii, US, pp 705–710.Google Scholar
15.Guzella, T.S., Mota-Santos, T.A. and Caminhas, W.M. A novel immune inspired approach to fault detection, Proceedings of ICARIS 2007, Lecture Notes in Computer Science, 2007, Vol 4628, Springer-Verlag Berlin Heidelberg, pp 107–118.Google Scholar
16.Perhinschi, M.G., Moncayo, H. and Davis, J.Integrated framework for artificial immunity-based aircraft failure detection, identification, and evaluation, AIAA J Airc, 2010, 47, (6), pp 1847–1859.Google Scholar
17.Perhinschi, M.G., Moncayo, H. and Al Azzawi, D.Integrated immunity-based framework for aircraft abnormal conditions management, AIAA J Airc, 2014, 51, (6), pp 1726–1739, doi: 10.2514/1.C032381.Google Scholar
18.Davis, J., Perhinschi, M.G. and Moncayo, H.Evolutionary algorithm for artificial immune system-based failure detector generation and optimization, AIAA J Guidance, Control, and Dynamics, 2010, 33, (2), pp 302–320.Google Scholar
19.Moncayo, H., Perhinschi, M.G. and Davis, J.Aircraft failure detection and identification using an immunological hierarchical multi-self strategy, AIAA J Guidance, Control, and Dynamics, 2010, 33, (4), pp 1105–1114.Google Scholar
20.Moncayo, H., Perhinschi, M.G. and Davis, J.Artificial immune system – based aircraft failure detection and identification over an extended flight envelope, Aeronaut J, 2011, 115, (1163), pp 43–55.Google Scholar
21.Moncayo, H., Perhinschi, M.G. and Davis, J. Simulation environment for the development and testing of immunity-based aircraft failure detection schemes, Proceedings of the AIAA Modeling and Simulation Technologies Conference, 2011, Portland, Oregon, US.Google Scholar
22.Moncayo, H., Perhinschi, M.G. and Davis, J.Artificial-immune-system-based aircraft failure evaluation over extended flight envelope, AIAA J Guidance, Control, and Dynamics, 2011, 34, (4), pp 989–1001.CrossRefGoogle Scholar
23.Dasgupta, D., Krishna Kumar, K., Wong, D. and Berry, M. Negative selection algorithm for aircraft fault detection, Proceedings of ICARIS 2004, LNCS3239, 2004, pp 1–13.Google Scholar
24.Janeway, C.A., Travers, P., Walport, M. and Shlomchik, M.J.Immunobiology: The Immune System in Health and Disease, 6th ed, 2005, Garland Science, New York, New York, US.Google Scholar
25.Moncayo, H. and Perhinschi, M.Aircraft Fault Tolerance: A Biologically Inspired Immune Framework for Sub-System Failures, 2011, VDM Verlag Dr. Muller GmbH & Co. KG, VDM Publishing House Ltd., Saarbruecken, Germany.Google Scholar
26.Perhinschi, M.G., Moncayo, H., Al Azzawi, D. and Moguel, I.Generation of artificial immune system antibodies using raw data and cluster set union, Int J Immune Computation, 2014, 2, (1), pp 1–15.Google Scholar
27.Perhinschi, M.G., Moncayo, H., Wilburn, B., Bartlett, A., Davis, J. and Karas, O.Neurally-augmented immunity- based detection and identification of aircraft sub-system failures, The Aeronaut J, 2014, 118, (1205), pp 775–796.Google Scholar
28.Perhinschi, M.G., Napolitano, M.R., Campa, G., Fravolini, M.L. and Seanor, B.Integration of sensor and actuator failure detection, identification, and accommodation schemes within fault tolerant control laws, Control and Intelligent Systems, 2007, 35, (4), pp 309–318Google Scholar