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Multiple Damage Progression Paths in Model-Based PrognosticsModel-based prognostics approaches employ domain knowledge about a system, its components, and how they fail through the use of physics-based models. Component wear is driven by several different degradation phenomena, each resulting in their own damage progression path, overlapping to contribute to the overall degradation of the component. We develop a model-based prognostics methodology using particle filters, in which the problem of characterizing multiple damage progression paths is cast as a joint state-parameter estimation problem. The estimate is represented as a probability distribution, allowing the prediction of end of life and remaining useful life within a probabilistic framework that supports uncertainty management. We also develop a novel variance control mechanism that maintains an uncertainty bound around the hidden parameters to limit the amount of estimation uncertainty and, consequently, reduce prediction uncertainty. We construct a detailed physics-based model of a centrifugal pump, to which we apply our model-based prognostics algorithms. We illustrate the operation of the prognostic solution with a number of simulation-based experiments and demonstrate the performance of the chosen approach when multiple damage mechanisms are active
Document ID
20110012032
Acquisition Source
Ames Research Center
Document Type
Conference Paper
Authors
Daigle, Matthew
(California Univ. Moffett Field, CA, United States)
Goebel, Kai Frank
(NASA Ames Research Center Moffett Field, CA, United States)
Date Acquired
August 25, 2013
Publication Date
March 3, 2011
Publication Information
ISBN: 978-1-4244-7351-9
Subject Category
Numerical Analysis
Report/Patent Number
ARC-E-DAA-TN2719
Meeting Information
Meeting: 2011 IEEE Aerospace Conference
Location: Big Sky, MT
Country: United States
Start Date: March 5, 2011
End Date: March 12, 2011
Funding Number(s)
CONTRACT_GRANT: NAS2-03144
Distribution Limits
Public
Copyright
Public Use Permitted.
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