Hierarchical Fault Diagnosis in Satellites Formation Flight

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Published Mar 26, 2021
Amitabh Barua K. Khorasani

Abstract

Ground-support based satellite health monitoring and fault diagnosis practices involve around-the- clock limit-checking and trend analysis on large amount of telemetry data. They do not scale well for future multi-platform space missions due to the size of the telemetry data and an increasing need to make the long-duration missions cost- effective by limiting the operations team personnel. To utilize telemetry data efficiently, and to assist the less-experienced personnel in perform- ing monitoring and diagnosis tasks, we have developed a hierarchical fault diagnosis methodology. The hierarchical decomposition is presented through a novel Bayesian Network (BN) whose structure is developed from the knowledge of component health state dependencies, and the parameters are obtained by a proposed methodology that utilizes both node fault diagnosis performance data and domain experts’ beliefs. Our proposed model development procedure reduces the demand for expert’s time in eliciting probabilities significantly, and our approach provides the ground personnel with an ability to perform diagnostic reasoning across a number of subsystems and components coherently. Due to the unavailability of real formation flight data, we demonstrate the effectiveness of our proposed methodology by using synthetic data of a leader-follower formation flight configuration. Although our proposed approach is developed from the satellite fault diagnosis perspective, it is generic and is targeted towards other types of cooperative fleet vehicle diagnosis problems.

How to Cite

Barua, A., & Khorasani, K. (2021). Hierarchical Fault Diagnosis in Satellites Formation Flight. Annual Conference of the PHM Society, 1(1). Retrieved from https://papers.phmsociety.org/index.php/phmconf/article/view/1582
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Keywords

fault diagnosis, applications: space

References
(Barua and Khorasani, 2007) A. Barua and K. Kho- rasani. Intelligent Model-based Hierarchical Fault Diagnosis for Satellite Formations. In Proc. 2007 IEEE International Conference on Systems, Man and Cybernetics (SMC 2007), Montreal, Canada, October 2007.
(Barua and Khorasani, 2008) A. Barua and K. Kho- rasani. Multi-Level Fault Diagnosis in Satellite Formations using Fuzzy Rule-Based Reasoning. In Proc. 2nd International Symposium on Systems and Control in Aeronautics and Astronauticss (ISSCAA 2008), Shenzhen, China, December 2008.
(Barua and Khorasani, 2009) A. Barua and K. Kho- rasani. Hierarchical Fault Diagnosis and Health Monitoring in Multi-platform Space Systems. In Proc. 2009 IEEE Aerospace Conference, Big Sky, Montana, USA, March 2009.
(Bednarskia et al., 2004) Marcin Bednarskia, Woj- ciech Cholewa, and Wiktor Frid. Identification of Sensitivities in Bayesian Networks. Engineering Applications of Artificial Intelligence, 17:327–335, 2004.
(Bialke, 1998) B. Bialke. High Fidelity Mathematical Modeling of Reaction Wheel Performance. Ad- vances in the Astronautical Sciences, 98:483–496, 1998.
(Davison and Bird, 2008) Craig. R. Davison and Jeff. W. Bird. Review of Metrics and Assignment of Confidence Intervals for Health Management of Gas Turbine engines. In Proc. ASME Turbo Expo 2008, Berlin, Germany, June 2008.
(Fenton et al., 2007) N. E. Fenton, M. Neil, and J. G. Caballero. Using Ranked Nodes to Model Qualitative Judgments in Bayesian Networks. IEEE Transactions on Knowledge and Data Engineering, 19, No. 10:1420–1432, October 2007.
(Henrion et al., 1996) Max Henrion, Malcolm Prad- han, Brendan Del Favero, Kurt Huang, Gregory Provan, and Paul O’Rorke. Why is Diagnosis using Belief Networks Insensitive to Imprecision in Probabilities? In Proc. 12th Annual Conference on Uncertainty in Artificial Intelligence, pages 307–314, Portland, Oregon, USA, August 1996.
(Hill, 1878) G. W. Hill. Researches in Luner Theory. American Journal of Mathematics, 1, No. 1:5–26, 1878.
(Iverson, 2008) D. L. Iverson. System Health Monitoring for Space Mission Operations. In Proc. 2008 IEEE Aerospace Conference, pages 1–8, Big Sky, Montana, USA, March 2008.
(Jensen and Nielsen, 2007) F. V. Jensen and T. D. Nielsen. Bayesian Networks and Decision Graphs. Springer, NY, USA, 2 edition, 2007.
(Jiang et al., 2003a) Z. Jiang, R. A. Dougal, and S. Liu. Application of VTB in Design and Testing of Satellite Electrical Power Systems. Journal of Power Sources, 122:95–108, 2003.
(Jiang et al., 2003b) Z. Jiang, S. Liu, and R. A. Dou- gal. Design and Testing of Spacecraft Power Systems Using VTB. IEEE Transactions on Aerospace and Electronic Systems, 39, No. 3:976–989, July 2003.
(Kurien and R-Moreno, 2008) J. Kurien and M. D. R- Moreno. Cost and Benefits of Model-based Diag- nosis. In Proc. 2008 IEEE Aerospace Conference, pages 1–14, Big Sky, Montana, USA, March 2008.
(Laskey and Mahoney, 2000) K. B. Laskey and S. M. Mahoney. Network Engineering for Agile Belief Network Models. IEEE Transactions on Knowl- edge and Data Engineering, 12, No. 4:487–498, July/August 2000.
(Nikovski, 2000) Daniel Nikovski. Constructing Bayesian Networks for Medical Diagnosis from In- complete and Partially Correct Statistics. IEEE Transactions on Knowledge and Data Engineering, 12, No. 4:509–516, July/August 2000.
(Pearl, 1988) Judea Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Monteo, CA, USA, 1988.
(Renooij, 2000) Silja Renooij. Probability Elicitation for Belief Networks: Issues to Consider. The Knowledge Engineering Review, 163, No. 3:255– 269, 2000.
(SAE, 2008) Health and Usage Monitoring Metrics, Monitoring the Monitor. Society of Automotive Engineers (SAE) Standard, Issuing Committee: E- 32 Aerospace Propulsion Systems Health Management, February 2008. Document No. ARP 5783.
(UCLA, 2009) Automated Reasoning UCLA. SAMIAM, Cited: July http://reasoning.cs.ucla.edu/samiam/.
(USC-VTB-Team, 2009) USC-VTB-Team.
lite Electrical Power System. USC Virtual Test Bed Homepage, Cited: June 2009. http://vtb.ee.sc.edu/applications/.
(Vadali et al., 2002) S. R. Vadali, S. S. Vaddi, and K. T. Alfriend. An Intelligent Control Concept for Formation Flying Satellites. International Journal of Robust and Nonlinear Control, 12:97–115, 2002.
(Wang, 2006) Haiqin Wang. Using Sensitivity Analysis to Validate Bayesian Networks for Airplane Subsystem Diagnosis. In Proc. 2006 IEEE Aerospace Conference, Big Sky, MT, USA, March 2006.
Section
Technical Papers