LEADER-an integrated engine behavior and design analyses based real-time fault diagnostic expert system for space shuttle main engine (SSME)

ABSTRACT

An expert system, called LEADER, has been designed and implemented for automatic learning, detection, identification, verification and correction of anomalous propulsion system operations in real time. LEADER employs a set of sensors to monitor engine component performance, and to detect, identify and validate abnormalities with respect to varying engine dynamics and behavior. Two diagnostic approaches are adopted in the architecture of LEADER. In the first approach fault diagnosis is performed through learning and identifying engine behavior patterns. LEADER, utilizing this approach, generates few hypotheses about the possible abnormalities. These hypotheses are then validated based on the SSME design and functional knowledge. The second approach directs the processing of engine sensory data and performs reasoning based on the SSME design, functional knowledge, and the deep-level knowledge, i.e., the first principles (physics and mechanics) of SSME subsystems and components. This paper describes LEADER's architecture which integrates a design-based reasoning approach with neural network-based fault pattern matching techniques. The fault diagnosis results obtained through the analyses of SSME ground test data are presented and discussed.

REFERENCES

• 1: Randall Davis, "Diagnosis via causal reasoning: Paths of interaction ad the locality principle, ~' Artificial intelligence Laboratory, MIT.
• 2: Raman Rajagopalan, "Qualitative Modeling in the Turbojet Engine Domain," M.S. Thesis, CSL Tech. Rept. T-139, Dept of Electrical Engineering, Univ. of Illinois, Urbana, IL, March 1984.
• 3: Jeff Yung-Choa Pan, "Qualitative Reasoning with deep-level mechanism models for fault diagnosis of mechanism failures, ~' IEEE proceedings, pp. 295-301, 1984.
• 4: M. Ali, D. A. Scharnhost, "Sensor-based fault diagnosis in a flight expert system," IEEE Proceedings of the Second Conference on Artificial Intelligence Applications, Miami, FL., pp. 49- 54, Dec. 11-13, 1985.
• 5: W. E. Dietz , E. L. Kiech , M. Ali, Pattern-based fault diagnosis using neural networks, Proceedings of the 1st international conference on Industrial and engineering applications of artificial intelligence and expert systems, p.13-23, June 1988, Tullahoma, Tennessee, United States [doi>10.1145/51909.51911]
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INDEX TERMS

Primary Classification:
  J. Computer Applications
  J.7 COMPUTERS IN OTHER SYSTEMS
      Subjects: Command and control

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.1 Applications and Expert Systems
          Subjects: Industrial automation

  J. Computer Applications
  J.2 PHYSICAL SCIENCES AND ENGINEERING
      Subjects: Aerospace

General Terms:
Design, Reliability