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Online model-based diagnosis to support autonomous operation of an advanced life support systemThis article describes methods for online model-based diagnosis of subsystems of the advanced life support system (ALS). The diagnosis methodology is tailored to detect, isolate, and identify faults in components of the system quickly so that fault-adaptive control techniques can be applied to maintain system operation without interruption. We describe the components of our hybrid modeling scheme and the diagnosis methodology, and then demonstrate the effectiveness of this methodology by building a detailed model of the reverse osmosis (RO) system of the water recovery system (WRS) of the ALS. This model is validated with real data collected from an experimental testbed at NASA JSC. A number of diagnosis experiments run on simulated faulty data are presented and the results are discussed.
Document ID
20050184248
Acquisition Source
Ames Research Center
Document Type
Reprint (Version printed in journal)
Authors
Biswas, Gautam
(Department of EECS and Institute for Software Integrated Systems, Vanderbilt University Nashville, TN 37235, United States)
Manders, Eric-Jan
Ramirez, John
Mahadevan, Nagabhusan
Abdelwahed, Sherif
Date Acquired
August 23, 2013
Publication Date
January 1, 2004
Publication Information
Publication: Habitation (Elmsford, N.Y.)
Volume: 10
Issue: 1
ISSN: 1542-9660
Subject Category
Man/System Technology And Life Support
Funding Number(s)
CONTRACT_GRANT: NAS2-37143
CONTRACT_GRANT: NCC9-159
Distribution Limits
Public
Copyright
Other
Keywords
Non-NASA Center
NASA Discipline Life Support Systems

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