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Machine learning of fault characteristics from rocket engine simulation dataTransformation of data into knowledge through conceptual induction has been the focus of our research described in this paper. We have developed a Machine Learning System (MLS) to analyze the rocket engine simulation data. MLS can provide to its users fault analysis, characteristics, and conceptual descriptions of faults, and the relationships of attributes and sensors. All the results are critically important in identifying faults.
Document ID
19960011791
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Ke, Min (Center for Advanced Space Propulsion Tullahoma, TN, United States)
Ali, Moonis (Center for Advanced Space Propulsion Tullahoma, TN, United States)
Date Acquired
August 17, 2013
Publication Date
November 2, 1990
Publication Information
Publication: Center for Advanced Space Propulsion Second Annual Technical Symposium Proceedings
Subject Category
Quality Assurance And Reliability
Accession Number
96N70676
Funding Number(s)
CONTRACT_GRANT: NAG1-513
Distribution Limits
Public
Copyright
Public Use Permitted.

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