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

Authors:
U. K. Gupta;M. Ali
Affiliations:
The Univ. of Tennessee Space Institute, Tullahoma;The Univ. of Tennessee Space Institute, Tullahoma
Venue:
IEA/AIE '89 Proceedings of the 2nd international conference on Industrial and engineering applications of artificial intelligence and expert systems - Volume 1
Year:
1989

Citing 2
Cited 0

Pattern-based fault diagnosis using neural networks

IEA/AIE '88 Proceedings of the 1st international conference on Industrial and engineering applications of artificial intelligence and expert systems - Volume 1
Hierarchical representation and machine learning from faulty jet engine behavioral examples to detect real time abnormal conditions

IEA/AIE '88 Proceedings of the 1st international conference on Industrial and engineering applications of artificial intelligence and expert systems - Volume 2

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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.