A diagnostic tree approach for fault cause identification in the attitude control subsystem of satellites

IEEE Transactions on Aerospace and Electronic Systems

Volumn 45, Issue 3, 2009, Pages 983-1002

  Barua, Amitabh ^a   Sinha, Purnendu ^a,b   Khorasani, Khashayar ^a  

^a Gina Cody School of Engineering and Computer Science   (Canada)

^b GENERAL MOTORS CORPORATION   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ATTITUDE CONTROL SUB-SYSTEM; CAUSAL MODEL; DIAGNOSTIC DECISIONS; DIAGNOSTIC TOOLS; EARTH OBSERVATION PROGRAMS; FAULT DIAGNOSIS; FAULT SCENARIOS; FAULT TYPES; FAULT-DETECTION MECHANISMS; FAULT-DIAGNOSIS SYSTEM; GROUND STATIONS; HUMAN OPERATOR; MACHINE-LEARNING; NATURALLY OCCURRING; POTENTIAL FAULTS; RELIABLE OPERATION; SPACE VEHICLES; SYNTHESIS ALGORITHMS;

ATTITUDE CONTROL; DECISION SUPPORT SYSTEMS; EARTH (PLANET); FAULT DETECTION; LEARNING ALGORITHMS; MATHEMATICAL OPERATORS; NAVIGATION; RELIABILITY; SATELLITES; SPATIAL VARIABLES CONTROL; VEHICLES;

FAULT TREE ANALYSIS;

EID: 72649085357     PISSN: 00189251     EISSN: None     Source Type: Journal    
DOI: 10.1109/TAES.2009.5259178     Document Type: Article

References (41)

1. Yairi, T., Nakatsugawa, M., Hori, K., Nakasuka, S., Machida, K., and Ishihama, N. Adaptive limit checking for spacecraft telemetry data using regression tree learning. In Proceedings of 2004 IEEE International Conference on Systems, Man and Cybernetics, The Hague, The Netherlands, Oct. 2004, 5130-5135.
2. Isermann, R. Model-based fault detection and diagnosis-status and applications. Annual Reviews in Control, 29, 1 (2005), 71-85.
3. Patton, R. J., and Chen, J. Review of parity space approaches to fault diagnosis for aerospace systems. Journal of Guidance, Control and Dynamics, 17, 2 (Mar.-Apr. 1994), 278-285.
4. Pervan, B. S., Lawrence, D. G., and Parkinson, B. W. Autonomous fault detection and removal using GPS carrier phase. IEEE Transactions on Aerospace and Electronic Systems, 34, 2 (July 1998), 897-906.
5. Jin, H., and Zhang, H. Y. Optimal parity vector sensitive to designated sensor fault. IEEE Transactions on Aerospace and Electronic Systems, 35, 4 (Oct. 1999), 1122-1128.
6. Skormin, V. A., Apone, J., and Dunphy, J. J. On-line diagnostics of a self-contained flight actuator. IEEE Transactions on Aerospace and Electronic Systems, 30, 1 (Jan. 1994), 186-196.
7. HanYong, H., and ZengQi, S. Fault detection and isolation of flight based on robust observer. In Fifth World Congress on Intelligent Control and Automation (WCICA), 2 (June 2004), 1642-1645.
8. Duan, G. R., and Patton, R. J. Robust Fault detection in linear systems using Luenberger observers. In UKACC International Conference on Control '98, Swansea, UK, Sept. 1998, 1468-1473.
9. Chang, C. T., and Hwang, J. I. Simplification techniques for EKF computations in fault diagnosis-suboptimal gains. Chemical Engineering Science, 53, 22 (1998), 3853-3862.
10. Xiong, K., Chan, C. W., and Zhang, H. Y. Detection of satellite attitude sensor faults using the UKF. IEEE Transactions on Aerospace and Electronic Systems, 43, 2 (Apr. 2007), 480-491.
11. Tudoroiu, N., and Khorasani, K. Satellite fault diagnosis using a bank of interactive Kalman filters. IEEE Transactions on Aerospace and Electronic Systems, 43, 4 (Oct. 2007), 1334-1350.
12. Graichen, C. M., and Cheetham, W. E. Case-based reasoning approaches for gas turbine trip diagnosis. In Proceedings of 2007 Gas Turbine Technical Congress and Exposition (ASME Turbo Expo 2007), Montreal, Canada, May 2007.
13. Saxena, A., Wu, B., and Vachtsevanos, G. Integrated diagnosis and prognosis architecture for fleet vehicles using dynamic case based reasoning. In Proceedings of the IEEE-Autotestcon, 2005, 96-102.
14. Fong, C. M., and Hui, S. C. An intelligent online machine fault diagnosis system. Computing and Control Engineering Journal, Oct. 2001.
15. Korbicz, J., Koscieny, J. M., Kowalczuk, Z., and Cholewa, W., Eds. Fault Diagnosis: Models, Artificial Intelligence, Applications, Germany: Springer, 2004.
16. Koscieny, J. M., and Syfert, M. Fuzzy Diagnostic reasoning that takes into account the uncertainty of the relation between faults and symptoms. International Journal of Applied Mathematics and Computer Science, 16, 1 (2006), 27-35.
17. Ligeza, A., and Fuster-Parra, P. A multi-level knowledge-based model of diagnostic reasoning. In P. J. Charrel (Ed.), Information Modelling and Knowledge Bases IX, Tuluza, France: IOS Press, 1998, 330-344.
18. Paako, M., and Myllymaki, P. Bayesian networks for advanced FDIR. In ESA Workshop on On-Board Autonomy, Noordwijk, The Netherlands, Oct. 2001.
19. Mourot, G., Bousghiri, S., and Ragot, J. Pattern recognition for diagnosis of technological systems: A review. In Proceedings of International Conference on Systems, Man and Cybernetics, Oct. 1993, 275-281.
20. Talebi, H. A., and Patel, R. A neural network-based fault detection scheme for satellite attitude control systems. In Proceedings of 2005 IEEE International Conference on Control Applications (CCA-2005), Toronto, Canada, Aug. 2005, 1293-1298.
21. Al-Zyoud, I. A., and Khorasani, K. Neural network-based actuator fault diagnosis for attitude control subsystem of an unmanned space vehicle. In Proceedings of IEEE International Joint Conference on Neural Networks (IJCNN-2006), Vancouver, Canada, July 2006.
22. Li, Z. Q., Ma, L., and Khorasani, K. A dynamic neural network-based reaction wheel fault diagnosis for satellites. In Proceedings of IEEE International Joint Conference on Neural Networks (IJCNN-2006), Vancouver, Canada, July 2006.
23. Lee, C., Alena, R. L., and Robinson, P. Migrating fault trees to decision trees for real time fault detection on international space station. In Proceedings of 2005 IEEE Aerospace Conference, Big Sky, MT, Mar. 2005, 1-6.
24. Juricic, D., Znidarsic, A., and Fussel, D. Generation of diagnostic trees by means of simplified process models and machine learning. Engineering Applications of Artificial Intelligence, 10, 1 (Feb. 1997), 15-29.
25. Sun, W., Chen, J., and Li, J. Decision tree and PCA-based fault diagnosis of rotating machinery. Mechanical Systems and Signal Processing, 21, 3 (Apr. 2007), 1300-1317.
26. Madden, M. G., and Nolan, P. J. Generation of fault trees from simulated incipient fault case data. In Proceedings of 9th International Conference on Applications of Artificial Intelligence in Engineering, Pennsylvania, 1994, 567-574.
27. Stanek, M., Morari, M., and Frohlich, K. Model-aided diagnosis: An inexpensive combination of model-based and case-based condition assessment. IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews, 31, 2 (May 2001), 137-145.
28. Nakkabi, Y., Kabbaj, N., Dahhou, B., Roux, G., and Aguilar-Martin, J. A combined analytical and knowledge based method for fault detection and isolation. In Proceedings of IEEE Conference on Emerging Technologies and Factory Automation (ETFA '03), vol.2, Sept. 2003, 161-164.
29. Joshi, A., Gavriloiu, V., Barua, A., Garabedian, A., Sinha, P., and Khorasani, K. Intelligent and learning-based approaches for health monitoring and fault diagnosis of RADARSAT-1 attitude control system. In Proceedings of 2007 IEEE International Conference on Systems, Man and Cybernetics (SMC-2007), Montreal, Canada, Oct. 2007.
30. Ericson, C. A. Fault tree analysis-A history. In Proceedings of 17th International System Safety Conference, Florida, Aug. 1999.
31. Vesely, W., Dugan, J., Fragola, J., Minarick, J., and Railsback, J. Fault tree handbook with aerospace applications. NASA Office of Safety And Mission Assurance, NASA Headquarters, Washington, D.C. 20546, Technical Report Version 1.1, Aug. 2002.
32. Larson, W. J., and Wertz, J. R. (Eds.) Space Mission Analysis and Design. Boston: Kluwer Academic Publishers, 1999.
33. Hughes, P. C. Spacecraft Attitude Dynamics. New York: Wiley, 1986.
34. Kuo, B. C. Automatic Control Systems (7th ed.). Upper Saddle River, NJ: Prentice-Hall, 1995.
35. Ogata, K. Modern Control Engineering (3rd ed.). Upper Saddle River, NJ: Prentice-Hall, 1996.
36. Barua, A., Sinha, P., and Khorasani, K. On the fault diagnosis and failure analysis in the satellite attitude control subsystem. In Proceedings of 8th International Conference on Space Operations (SpaceOps2004), Montreal, Canada, May 2004.
37. Barua, A., Sinha, P., Khorasani, K., and Tafazoli, S. A novel fault-tree approach for identifying potential causes of satellite reaction wheel failure. In Proceedings of 2005 IEEE International Conference on Control Applications (CCA-2005), Toronto, Canada, Aug. 2005, 1467-1472.
38. Quinlan, J. R. Induction of decision trees. Machine Learning, 1 (1986), 81-106.
39. Barlett, L. M., and Andrews, J. D. Efficient basic event orderings for binary decision diagrams. In Proceedings of Annual Reliability and Maintainability Symposium, Jan. 1998, 61-68.
40. Barlett, L. M., and Andrews, J. D. Choosing a heuristic for the 'fault tree to binary decision diagram' conversion. IEEE Transactions on Reliability, 51, 3 (Sept. 2002), 344-349.
41. Bialke, B. High fidelity mathematical modeling of reaction wheel performance. Advances in the Astronautical Sciences, 98 (1998), 483-496.