Hostname: page-component-76fb5796d-vfjqv Total loading time: 0 Render date: 2024-04-27T17:36:27.644Z Has data issue: false hasContentIssue false
  • English
  • Français

Model-based diagnosis of the space shuttle main engine

Published online by Cambridge University Press:  27 February 2009

Martin O. Hofmann ,
Thomas L. Cost  and
Michael Whitley
Martin O. Hofmann
Affiliation:
Department of Electrical and Computer Engineering
Thomas L. Cost
Affiliation:
Department of Mechanical Engineering, University of Alabama in Huntsville, Huntsville, AL 35899
Michael Whitley
Affiliation:
NASA-MSFC, Huntsville, AL 35812, U.S.A.
Get access Rights & Permissions [Opens in a new window]

Abstract

The process of reviewing test data for anomalies after a firing of the Space Shuttle Main Engine (SSME) is a complex, time-consuming task. A project is under way to provide the team of SSME experts with a knowledge-based system to assist in the review and diagnosis task. A model-based approach was chosen because it can be adapted to changes in engine design, is easier to maintain, and can be explained more easily. A complex thermodynamic fluid system like the SSME introduces problems during modeling, analysis, and diagnosis which have as yet been insufficiently studied. We developed a qualitative constraint-based diagnostic system inspired by existing qualitative modeling and constraint-based reasoning methods which addresses these difficulties explicitly. Our approach combines various diagnostic paradigms seamlessly, such as the model-based and heuristic association-based paradigms, in order to better approximate the reasoning process of the domain experts. The end-user interface allows expert users to actively participate in the reasoning process, both by adding their own expertise and by guiding the diagnostic search performed by the system.

Type
Research Article
Information
AI EDAM , Volume 6 , Issue 3 , August 1992 , pp. 131 - 148
Copyright
Copyright © Cambridge University Press 1992

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Adams, E. W. 1975. The Logic of Conditionals, Dordrecht, Holland: D. Reidel.CrossRefGoogle Scholar
Biswas, G., Hagins, W. J. and Debelak, K. A. 1989. Qualitative modeling in engineering applications, Proceedings of the 1989 IEEE International Conference on Systems, Man, and Cybernetics, Cambridge, MA, November 1989, pp. 997–1002.Google Scholar
Buttner, K., Sriram, D. and Freiling, M. 1989. An object-oriented blackboard architecture for model-based diagnostic reasoning. In Blackboard Architectures and Applications, ed by Jagannathan, V., Dodhiwala, R., and Baum, L., San Diego, CA: Academic Press, 403–431.Google Scholar
Cost, T. L. and Hofmann, M. O. 1990. Engine Data Interpretation System, UAH Research Report, No. ME-90–101, July 1990.Google Scholar
Davis, E. 1987. Constraint propagaton with interval labels. Artificial Intelligence, 32(3), 281–331.CrossRefGoogle Scholar
Davis, R. 1984. Diagnostic reasoning based on structure and behavior. Artificial Intelligence, 24(1–3), 347–410.CrossRefGoogle Scholar
Davis, R. and Hamscher, W. 1988. Model-based reasoning: troubleshooting. In Exploring Artificial Intelligence, ed. by Shrobe, E. H., California: Morgan Kaufmann, 297–346.CrossRefGoogle Scholar
de Kleer, J. and Brown, J. S. 1985. A qualitative physics based on confluences. In Qualitative Reasoning about Physical Systems, ed. by Bobrow, D. G., Cambridge, MA: MIT Press, 7–83.Google Scholar
de Kleer, J., 1986. An assumption-based TMS. Artificial Intelligence, 28(2), 127–162.CrossRefGoogle Scholar
de Kleer, J. and Williams, B. C. 1987. Diagnosing multiple faults. Artificial Intelligence, 32(1), 97–130.CrossRefGoogle Scholar
Forbus, K. D. 1984. Qualitative process theory. Artificial Intelligence, 24(1–3), 85–168.CrossRefGoogle Scholar
Forbus, K. D. 1988. Qualitative physics: past, present, and future. In Exploring Artificial Intelligence, ed. by Shrobe, E. H., California: Morgan Kaufmann, 239–296.CrossRefGoogle Scholar
Gallanti, M., Stefanini, A., and Tomada, L., 1989. ODS: a diagnostic system based on qualitative modeling techniques. Proceedings of the Fifth Conference on Artificial Intelligence Applications, Miama, FL, 142–149.Google Scholar
Govindaraj, T. 1987. Qualitative approximation methodology for modeling and simulation of large dynamic systems: applications to a marine power plant. IEEE Transactions SMC, SMC-17 (6), 937–955.Google Scholar
Gupta, U. K. and Ali, M. 1988. LEADER—an integrated engine behavior and design analysis based real time fault diagnostic expert system for shuttle main engine. Proceedings of the 2nd IEA/AIE, Tullahoma, Tennessee, 135–145.Google Scholar
Hamscher, W., 1990 a. Modeling digital circuits for troubleshooting: an overview. Proceedings of the Sixth Conference on Artificial Intelligence Applications, Santa Barbara, CA, 1, 2–8.CrossRefGoogle Scholar
Hamscher, W., 1990 b. XDE: diagnosing devices with hierarchic structure and known component failure modes. Proceedings of the Sixth Conference on Artificial Intelligence Applications, Santa Barbara, CA, 1, 48–54.CrossRefGoogle Scholar
Iwasaki, Y. and Simon, H. A. 1986. Causality in device behavior. Artificial Intelligence, 29(1), 3–32.CrossRefGoogle Scholar
Kalagnanam, J., Simon, H. and Iwasaki, Y. 1991. The mathematical bases for qualitative reasoning. IEEE Expert, 6(2), 11–19.CrossRefGoogle Scholar
Kuipers, B. 1985. Commonsense reasoning about causality: deriving behavior from structure. In Qualitative Reasoning about Physical Systems, ed. by Bobrow, D. G., Cambridge, MA: MIT Press, 169–203.Google Scholar
Luce, H. H. and Govind, R. L. 1989. Prediction and diagnosis of failure in the SSME high pressure fuel turbopump using backpropagation neural networks. Proceedings of the first Health Monitoring Conference for Space Propulsion Systems, Cincinnati, OH, 218–237.Google Scholar
Luce, H. H. and Govind, R. L. 1990. Neural netwrok pattern recognizer for detection of failure modes in the SSME. AIAA Paper No. 90–1893, AIAA 26th Joint Propulsion Conference, Boston, MA.CrossRefGoogle Scholar
Norman, A. M. and Taniguchi, M. 1988. Development of an advanced failure detection algorithm for the SSME. AIAA Paper No. 88–3408, AIAA 24th Joint Propulsion Conference, Boston, MA.CrossRefGoogle Scholar
Perry, J. G. 1988. An expert systems approach to turbopump health monitoring, AIAA paper 88–3117, AIAA 24th Joint Propulsion Conference, Boston, MA.CrossRefGoogle Scholar
Reiter, R. 1987. A theory of diagnosis from first principle. Artificial Intelligence, 32(1), 57–95.CrossRefGoogle Scholar
Stallman, R. M. and Sussman, G. J. 1977. Forward reasoning and dependency-directed backtracking in a system for computer-aided circuit analysis. Artificial Intelligence, 9, 135–196.CrossRefGoogle Scholar
Stefik, M., Aikins, J., Balzer, R., Benoit, J., Birnbaum, L., Hayes-Roth, F., and Sacerdoti, E. 1983. The architecture of expert systems. In Building Expert Systems, ed. by, Hayes-Roth, F., Waterman, D. A., and Lenat, D. B., Reading, MA: Addison-Wesley, 89–126.Google Scholar
Struss, P., 1989. New techniques in model-based diagnosis. Proceedings of the Second International Conference KBCS ’89, Bombay, India, 428–437.Google Scholar
Walker, B. K. and Baumgartner, E. T. 1990. Comparison of nonlinear smoothers and nonlinear estimators for rocket engine health monitoring. AIAA Paper No. 90–1891, AIAA 26th Joint Propulsion Conference.CrossRefGoogle Scholar
Whitehead, B., Kiech, E. and Ali, M. 1990. Rocket engine diagnostics using neural networks. AIAA Paper No. 90–1892, AIAA 26th Joint Propulsion Conference.CrossRefGoogle Scholar