Online model-based diagnosis to support autonomous operation of an advanced life support system.
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.
Biswas
Gautam
G
Department of EECS and Institute for Software Integrated Systems, Vanderbilt University, Nashville, TN 37235, USA. Biswas@eecsmail.vuse.vanderbilt.edu
Manders
Eric-Jan
EJ
Ramirez
John
J
Mahadevan
Nagabhusan
N
Abdelwahed
Sherif
S
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003661:Decision Support Techniques; D018504:Ecological Systems, Closed; D004869:Equipment Safety / Q000295:instrumentation*; D008021:Life Support Systems / Q000295:instrumentation*; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D009995:Osmosis; D013026:Space Flight; D013598:Systems Theory; D018508:Water Purification / Q000295:instrumentation*