Multi-Parameter Algorithm to Enhance Real-Time Space Shuttle Main Propulsion System Fault Detection
Document Number: 951398
Date Published: May 1995
Author(s):
Claudia M. Meyer - NYMA, Inc.
William A. Maul - NYMA, Inc.
Kenneth J. Dwyer - NASA Johnson Space Center
June F. Zakrajsek - NASA Lewis Research Center
Abstract:
Real-time algorithms which provide the earliest possible indication of off-nominal Space Shuttle Main Engine (SSME) conditions could improve shuttle safety and reliability by providing more time for corrective action. Multi-parameter fault detection techniques have been targeted because they do not rely on a single parameter for fault information and thereby improve confidence in the detection. Furthermore, no assumptions regarding failure modes are required, permitting the detection of previously unencountered or unanticipated failures. The Clustering Algorithm, a multi-parameter fault detection approach that was originally trained and validated on SSME ground test firing data, was slightly modified and applied to SSME historical flight data; the application is documented in this report. Preliminary studies were conducted to assess the impact of different engines, different missions and different thrust profiles on the performance of the Clustering Algorithm. The algorithm successfully predicted sixteen performance parameters during mainstage operation of the engine when applied to nominal data sets and provided indications of off-nominal behavior when applied to data from an engine which had experienced an offset in one of the control parameters. The information from the Clustering Algorithm is intended to enhance the diagnostic information available to the NASA Johnson Space Center control room engineers during flight.
File Size: 849K
Product Status: In Stock
See other papers presented at Aerospace Atlantic Conference & Exposition, May 1995, Dayton, OH, USA, Session: Aerospace Atlantic Conference & Exposition
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