Artificial intelligence techniques for ground test monitoring of rocket enginesAn expert system is being developed which can detect anomalies in Space Shuttle Main Engine (SSME) sensor data significantly earlier than the redline algorithm currently in use. The training of such an expert system focuses on two approaches which are based on low frequency and high frequency analyses of sensor data. Both approaches are being tested on data from SSME tests and their results compared with the findings of NASA and Rocketdyne experts. Prototype implementations have detected the presence of anomalies earlier than the redline algorithms that are in use currently. It therefore appears that these approaches have the potential of detecting anomalies early eneough to shut down the engine or take other corrective action before severe damage to the engine occurs.
Document ID
19900055094
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Ali, Moonis (Tennessee Univ. Space Inst. Tullahoma, TN, United States)
Gupta, U. K. (Tennessee, University Tullahoma, United States)