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Adaptive Fault Detection on Liquid Propulsion Systems with Virtual Sensors: Algorithms and ArchitecturesPrior to the launch of STS-119 NASA had completed a study of an issue in the flow control valve (FCV) in the Main Propulsion System of the Space Shuttle using an adaptive learning method known as Virtual Sensors. Virtual Sensors are a class of algorithms that estimate the value of a time series given other potentially nonlinearly correlated sensor readings. In the case presented here, the Virtual Sensors algorithm is based on an ensemble learning approach and takes sensor readings and control signals as input to estimate the pressure in a subsystem of the Main Propulsion System. Our results indicate that this method can detect faults in the FCV at the time when they occur. We use the standard deviation of the predictions of the ensemble as a measure of uncertainty in the estimate. This uncertainty estimate was crucial to understanding the nature and magnitude of transient characteristics during startup of the engine. This paper overviews the Virtual Sensors algorithm and discusses results on a comprehensive set of Shuttle missions and also discusses the architecture necessary for deploying such algorithms in a real-time, closed-loop system or a human-in-the-loop monitoring system. These results were presented at a Flight Readiness Review of the Space Shuttle in early 2009.
Document ID
Document Type
Conference Paper
Matthews, Bryan L. (SGT, Inc. Moffett Field, CA, United States)
Srivastava, Ashok N. (NASA Ames Research Center Moffett Field, CA, United States)
Date Acquired
August 25, 2013
Publication Date
May 3, 2010
Subject Category
Spacecraft Propulsion and Power
Report/Patent Number
Meeting Information
7th Modeling and Simulation Subcommittee (MSS) Joint Meeting(Colorado Springs, CO)
Funding Number(s)
Distribution Limits
Public Use Permitted.

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