Application of a Model-Based Fault Detection Approach on a Spacecraft

Document Type : Original Research Article

Authors

1 Aerospace Research Institute, Ministry of Science, Research and Technology, Tehran, Iran

2 Department of the Aerospace Engineering, K.N. Toosi University of Technology, Tehran, Iran

Abstract

The model-based fault detection approach is one of the software-based supervision systems monitoring. This method has a marked effect to detect components fault without demanding extra sensors to measure or add redundancy. The extended multiple model’s adaptive estimation method is an online strategy to detect and isolation failure of components. Simple implementation, fast and accurate response, compatibility with nonlinear systems, and the ability to detect different types of faults are the most important features of this method.  This method is applied to the faulty spacecraft in terms of actuators and its capability is evaluated. The most probable actuator fault implemented using MATLAB/SIMULINK software. The presented approach successfully detects faulty actuators.

Keywords


[1]    Rolf Isermann, Model-based fault-detection, and diagnosis status and applications, Annual Reviews in Control, Volume 29, Issue 1,2005, Pages 71-85, ISSN 1367-5788,
[2]    X. Yu, J. Jiang, A survey of fault-tolerant controllers based on safety-related issues, Annual Reviews in Control 39 (2015) 46–57.
[3]    Said, M., Abdellafou, K.b. & Taouali, O. Machine learning technique for data-driven fault detection of nonlinear processes. J Intell Manuf 31, 865–884 (2020).
[4]    S. Anwar, W. Niu, A nonlinear observer-based analytical redundancy for predictive fault- tolerant control of a steer-by-wire system, Asian Journal of Control 16 (2) (2014) 321–334.
[5]    Belsak, A., Flasker, J. (2008). Vibration analysis to determine the condition of gear units. Strojniški Vestnik - Journal of Mechanical Engineering, vol. 54, no. 1, p. 11-24
[6]    Capozzoli, A., F. Lauro, and I. Khan. 2015. “Fault Detection Analysis Using Data MiningTechniques for a Cluster of Smart Office Buildings.” Expert Systems with Applications. 42:4324–4338.
[7]    Bouallègue,W.; Bouslama Bouabdallah, S.; Tagina, M. Causal approaches and fuzzy logic in FDI of Bond Graph uncertain parameters systems. In Proceedings of the IEEE International Conference on Communications, Computing and Control Applications (CCCA), Hammamet, Tunisia, 3–5 March 2011
[8]    Thirumarimurugan, M.; Bagyalakshmi, N.; Paarkavi, P. Comparison of fault detection and isolation methods: A review. In Proceedings of the 2016 10th International Conference on Intelligent Systems and Control (ISCO), Coimbatore, India, 7–8 January 2016.
[9]    Li, Xifeng & Xie, Yongle & Bi, Dongjie & Ao, Yongcai. (2013). Kalman Filter-Based Method for Fault Diagnosis of Analog Circuits. Metrology and Measurement Systems. 20. 10.2478/mms-2013-0027.
[10] Skliros, C., Esperon Miguez, M., Fakhre, A., Jennions, I. (2019). A review of model-based and data-driven methods targeting hardware systems diagnostics. Diagnostyka, 20(1), 3-21. https://doi.org/10.29354/diag/99603
[11] Geliel, Mostafa & Zakzouk, Sherief & Sengaby, M.. (2012). Application of model-based fault detection for an industrial boiler. 2012 20th Mediterranean Conference on Control and Automation, MED 2012 - Conference Proceedings. 98-103. 10.1109/MED.2012.6265621.
[12] E. Khalastchi and M. Kalech. A sensor-based approach for fault detection and diagnosis for robotic systems. Autonomous Robots, 42(6):1231–1248, Aug. 2018.
[13] Patton, R.; Uppal, F.; Simani, S.; Polle, B. Robust FDI applied to thruster faults of a satellite system. Control Eng. Pract. 2010, 18, 1093–1109.
[14] Falcoz, A.; Henry, D.; Zolghadri, A. Robust Fault Diagnosis for Atmospheric Reentry Vehicles: A Case Study. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 2010, 40, 886–899.
[15] R. Isermann. Fault-Diagnosis Systems, An Introduction from Fault Detection to Fault Tolerance. Springer-Verlag, Berlin Heidelberg, 2006
[16] L. Ni.Fault-Tolerant Control of Unmanned Underwater Vehicles.PhDthesis, VA Tech. Univ., Blacksburg, VA, 2001.
[17] M. Azam, K. Pattipati, J. Allanach, S. Poll, and A. Petterson-Hine. In-Flight Fault Detection and Isolation in Aircraft Flight Control Systems. In Proceedings of IEEE Aerospace Conference, 2005. Paper 1429.
[18] G. Ducard and H. P. Geering, "A reconfigurable flight control system based on the EMMAE method," 2006 American Control Conference, Minneapolis, MN, 2006, pp. 6 pp.-, DOI: 10.1109/ACC.2006.1657599.
[19] Ducard, G.. (2009). Fault-tolerant Flight Control and Guidance Systems: Practical Methods for Small Unmanned Aerial Vehicles. 10.1007/978-1-84882-561-1.
[20] M.S. Grewall, A.P.Andrews. Kalman Filtering. Theory and Practice. Prentice-Hall, 1993
[21] MarcelSidi, “SpacecraftDynamics &Control, “ Cambridge university press, 1997
[22] R. Cowen, “The wheels come off Kepler,” Nature, vol. 497, no. 7450, pp. 417–418, May2013.
[23] Ghasem Sharifi & Ehsan Zabihian (2020) An effective approach to identify the mass properties of a satellite attitude dynamics simulator, Australian Journal of Mechanical Engineering, 18:3, 245254, DOI: 10.1080/14484846.2018.1458811
[24] A. Mohammad and S. M. B. Billah, "Analysis of speed control of series DC motor using diverter and observation of speed saturation point," 2015 International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), Dhaka, 2015, pp. 1-4, DOI: 10.1109/ICEEICT.2015.7307350.
[25] A. Adouni, A. Abid and L. Sbita, "A DC motor fault detection, isolation and identification based on a new architecture Artificial Neural Network," 2016 5th International Conference on Systems and Control (ICSC), Marrakesh, 2016, pp. 294-299, DOI: 10.1109/ICoSC.2016.7507054.