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This 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.
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Keywords: Advanced life support systems; Detection; Estimation; Fault diagnosis; Reverse osmosis system; Water recovery system

Document Type: Research Article

Affiliations: Department of EECS and Institute for Software Integrated Systems, Vanderbilt University, Nashville, TN 37235

Publication date: January 1, 2004

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  • Habitation, International Journal for Human Support Research, is designed to meet the needs of an emerging field of study necessitated by the need to develop new technologies to support human activities within controlled environments.
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