The detection and diagnosis of SSME faults in an early stage is important in order to allow enough time for fault preventive or corrective measurements. Since most of the faults in a complex system like SSME develop rapidly, early detection and diagnosis of faults is critical for the survival of space vehicles. An expert system has been designed for automatic learning, detection, identification, verification, and correction of anomalous propulsion system operations. This paper describes an innovative machine learning approach which is employed for the automatic training of this expert system.
An expert system for fault diagnosis in a Space Shuttle main engine
1990-07-01
Conference paper
No indication
English
Fault diagnosis for the Space Shuttle main engine
NTRS | 1992
|Fault diagnosis for the Space Shuttle main engine
AIAA | 1992
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