Model-based diagnostics for propellant loading systems

The loading of spacecraft propellants is a complex, risky operation. Therefore, diagnostic solutions are necessary to quickly identify when a fault occurs, so that recovery actions can be taken or an abort procedure can be initiated. Model-based diagnosis solutions, established using an in-depth ana...

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Bibliographic Details
Published in:2011 Aerospace Conference pp. 1 - 11
Main Authors: Daigle, Matthew, Foygel, Michael, Smelyanskiy, Vadim
Format: Conference Proceeding
Language:English
Published: Ames Research Center IEEE 01-03-2011
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Summary:The loading of spacecraft propellants is a complex, risky operation. Therefore, diagnostic solutions are necessary to quickly identify when a fault occurs, so that recovery actions can be taken or an abort procedure can be initiated. Model-based diagnosis solutions, established using an in-depth analysis and understanding of the underlying physical processes, offer the advanced capability to quickly detect and isolate faults, identify their severity, and predict their effects on system performance. We develop a physics-based model of a cryogenic propellant loading system, which describes the complex dynamics of liquid hydrogen filling from a storage tank to an external vehicle tank, as well as the influence of different faults on this process. The model takes into account the main physical processes such as highly non-equilibrium condensation and evaporation of the hydrogen vapor, pressurization, and also the dynamics of liquid hydrogen and vapor flows inside the system in the presence of helium gas. Since the model incorporates multiple faults in the system, it provides a suitable framework for model-based diagnostics and prognostics algorithms. Using this model, we analyze the effects of faults on the system, derive symbolic fault signatures for the purposes of fault isolation, and perform fault identification using a particle filter approach. We demonstrate the detection, isolation, and identification of a number of faults using simulation-based experiments.
Bibliography:Big Sky, MT
ARC
ARC-E-DAA-TN2715
Ames Research Center
IEEEAC Paper 1436
ISBN:1424473500
9781424473502
9781424473519
1424473519
ISSN:1095-323X
2996-2358
DOI:10.1109/AERO.2011.5747596