Model-based diagnosis of the space shuttle main engine

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.

[1]  E. W. Adams,et al.  The logic of conditionals , 1975 .

[2]  U. K. Gupta,et al.  LEADER-an integrated engine behavior and design analyses based real-time fault diagnostic expert system for space shuttle main engine (SSME) , 1989, IEA/AIE '89.

[3]  W. Hamscher,et al.  XDE: diagnosing devices with hierarchic structure and known component failure modes , 1990, Sixth Conference on Artificial Intelligence for Applications.

[4]  Johan de Kleer,et al.  An Assumption-Based TMS , 1987, Artif. Intell..

[5]  H. E. Shrobe,et al.  Exploring Artificial Intelligence , 1988 .

[6]  Randall Davis,et al.  Diagnostic Reasoning Based on Structure and Behavior , 1984, Artif. Intell..

[7]  J. Dekleer An assumption-based TMS , 1986 .

[8]  Johan de Kleer,et al.  A Qualitative Physics Based on Confluences , 1984, Artif. Intell..

[9]  V. Jagannathan,et al.  Blackboard Architectures and Applications , 1989 .

[10]  John G. Perry,et al.  An expert system approach to turbopump health monitoring , 1988 .

[11]  M. Gallanti,et al.  ODS: a diagnostic system based on qualitative modeling techniques , 1989, [1989] Proceedings. The Fifth Conference on Artificial Intelligence Applications.

[12]  Ernest Davis,et al.  Constraint Propagation with Interval Labels , 1987, Artif. Intell..

[13]  Kenneth D. Forbus Qualitative physics: past present and future , 1988 .

[14]  M. Taniguchi,et al.  Development of an advanced failure detection algorithm for the SSME , 1988 .

[15]  Brian C. Williams,et al.  Diagnosing Multiple Faults , 1987, Artif. Intell..

[16]  Eric Baumgartner,et al.  Comparison of nonlinear smoothers and nonlinear estimators for rocket engine health monitoring , 1990 .

[17]  Peter Struss New Techniques in Model-Based Diagnosis , 1989, KBCS.

[18]  D. Bobrow Qualitative Reasoning about Physical Systems , 1985 .

[19]  Benjamin Kuipers,et al.  Commonsense Reasoning about Causality: Deriving Behavior from Structure , 1984, Artif. Intell..

[20]  Herbert A. Simon,et al.  Causality in Device Behavior , 1989, Artif. Intell..

[21]  Kenneth D. Forbus Chapter 7 – Qualitative Physics: Past, Present, and Future , 1988 .

[22]  Bruce A. Whitehead,et al.  Rocket engine diagnostics using neural networks , 1990 .

[23]  Mark Fox Proceedings of the sixth conference on Artificial intelligence applications , 1990 .

[24]  Martin O. Hofmann,et al.  Engine Data Interpretation System (EDIS) , 1990 .

[25]  W. Hamscher,et al.  Modeling digital circuits for trouble-shooting: an overview , 1990, Sixth Conference on Artificial Intelligence for Applications.

[26]  Lawrence Birnbaum,et al.  The architecture of expert systems , 1983 .

[27]  Gautam Biswas,et al.  Qualitative modeling in engineering applications , 1989, Conference Proceedings., IEEE International Conference on Systems, Man and Cybernetics.

[28]  Raymond Reiter,et al.  A Theory of Diagnosis from First Principles , 1986, Artif. Intell..

[29]  T. Govindaraj Qualitative approximation methodology for modeling and simulation of large dynamic systems: Applications to a marine steam power plant , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[30]  Walter Hamscher,et al.  Modeling Digital Circuits for Troubleshooting , 1991, Artif. Intell..

[31]  Randall Davis,et al.  Model-based reasoning: troubleshooting , 1988 .

[32]  Frederick Hayes-Roth,et al.  Building expert systems , 1983, Advanced book program.

[33]  Kenneth D. Forbus Qualitative Process Theory , 1984, Artificial Intelligence.

[34]  Hudson Luce,et al.  Neural network pattern recognizer for detection of failure modes in the SSME , 1990 .

[35]  中園 薫 A Qualitative Physics Based on Confluences , 1986 .

[36]  Gerald J. Sussman,et al.  Forward Reasoning and Dependency-Directed Backtracking in a System for Computer-Aided Circuit Analysis , 1976, Artif. Intell..

[37]  Herbert A. Simon,et al.  The mathematical bases for qualitative reasoning , 1991, IEEE Expert.