Abstract
The need for Unmanned Aerial Vehicles (UAVs) is increasing as they are being used across the world for various civil, defense and aerospace applications such as surveillance, remote sensing, rescue, geographic studies, and security applications. The functionalities provided by the system is based on the system health. Monitoring the health of the system such as healthy, degraded (partially healthy or partially unhealthy) and unhealthy accurately without any impact on safety and security is of utmost importance. Hence in order to monitor the health of the system to provides the functionality for a longer period of time system fault detection and isolation techniques should be incorporated. This paper discusses Fault Detection and Isolation (FDI approach used in Unmanned Aerial Vehicle (UAV) autopilot to make its functionality more robust and available for a longer period of time. We proposes an integrated Adaptive Markov Model Analysis (AMMA) to detect and isolate faults in critical components of the system. The effectiveness of the novel approach is demonstrated by simulating the modified system design with FDI incorporation for the UAV autopilot. The proposed FDI approach helps in identifying the gyro sensor failure and provides a degraded mode to the system functionality which did not exist earlier in the design. The simulation demonstrates the system modes such as healthy, degraded (partially healthy or partially unhealthy) and unhealthy to understand the functionality better as the current design which works in only two modes i.e. healthy and unhealthy.
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References
Pullum, L.L.: Software Fault Tolerance Techniques and Implementation, pp. 1–358 (2001). http://www.artechhouse.com
Baoan, Li, Dawei, D.: Automatic test system for large unmanned aerial vehicle. IEEE, pp. 1–4 (2014)
Kumar, Nilesh, Jain, Sheilza: Identification Modeling and Control of Unmanned Aerial Vehicles. International Journal of Advanced Science and Technology 67, 1–10 (2014)
Austin, R.: Design, UAVs Development and Deployment. Willey Publication, pp. 1–399 (2010)
Hoffer, N.V., Coopmans, C., Jensen, A.M., Chen, Y.Q.: International Conference on Unmanned Aircraft Systems (ICUAS), pp. 897–903 (2013)
Sadeghzadeh, I., Zhang, Y.: A Review on Fault-Tolerant Control for Unmanned Aerial Vehicles (UAVs). American Institute of Aeronautics and Astronautics, pp. 1–12 (2011)
Marzat, J, Piet-Lahanier, H., Damongeot, F., Walter, E.: Model-based fault diagnosis for aerospace systems: a survey, In: Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 1–31 (2011)
Supervision, I.: Fault- Detection and Fault – Diagnosis Methods- an Introduction. Elsevier science Ltd, pp. 639–652 (1997)
Ghahramani.Z.: An Introduction to Hidden Markov Models and Bayesian Networks. International Journal for Pattern Recognition and Artificial Intelligence, 1–25 (2001)
Lv, F., Li, X., Wang, X.-Q.: A survey of intelligent network fault diagnosis technology. In: 25th Chinese Control and Decision Conference (CCDC), pp. 4874–4879, (2013)
Grewal, M., Andrews, A.: How Good Is Your Gyro? IEEE Control Systems Magazine, 1–4, February 2010
G200 Dual Axis MEMS Gyro User Guide. Gladiator Technologies (2012- 2014), pp. 1–36
Lopez-Estrada, F.R., Ponsart, J-C, Theillio, D., Astorga-Zaragoza, C.M., Zhang, Y.M.: Robust sensor fault diagnosis and tracking controller for a UAV modelled as LPV system. In: International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1311–1316, May 2014
Hu, B., Seiler, P.: Worst-case false alarm analysis of fault detection systems. In: American Control Conference (ACC), pp. 654–659, June 2014
Ahmed Nagy, Mercy Njima and Lusine Mkrtchyan.: A bayesian based method for agile software development release planning and project health monitoring. In: International Conference on Intelligent Networking and Collaborative Systems, pp. 192–199 (2010)
Berenji, H.R., Wang, Y.: Wavelet neural networks for fault diagnosis and prognosis. In: IEEE International Conference on Fuzzy Systems, pp. 1334–1339 (2006)
Kapadia, R., Stanley, G., Walker, M.: Real World Model-based Fault Management, pp. 1–8
Rabiner, L.R., Juang, B.H.: An Introduction to Hidden Markov Models. IEEE ASSP Magazine, 4–16 (1986)
Le, T.T., Chatelain, F., Berenguer, C.: Hidden Markov Models for Diagnostics and Prognostics of Systems under Multiple Deterioration Modes, pp. 1–9, July 2014
Stamp, M.: A Revealing Introduction to Hidden Markov Models, pp. 1–20, September 2012
Narasimhan, S., Choudhury, I.R., Balaban, E., Saxena, A.: combining model-based and feature-driven diagnosis approaches – a case study on electromechanical actuators. In: 21st International Workshop on Principles of Diagnosis, pp. 1–8 (2010)
Shang, Y.: The limit behavior of a stochastic logistic model with individual time dependent rates. Journal of Mathematics, 1–8 (2013). http://dx.doi.org/10.1155/2013/502635
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Krishnaprasad, R., Nanda, M., Jayanthi, J. (2016). Adaptive Markov Model Analysis for Improving the Design of Unmanned Aerial Vehicles Autopilot. In: Berretti, S., Thampi, S., Srivastava, P. (eds) Intelligent Systems Technologies and Applications. Advances in Intelligent Systems and Computing, vol 384. Springer, Cham. https://doi.org/10.1007/978-3-319-23036-8_22
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DOI: https://doi.org/10.1007/978-3-319-23036-8_22
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