Distributed Damage Estimation for Prognostics based on Structural Model Decomposition

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Matthew Daigle Anibal Bregon Indranil Roychoudhury

Abstract

Model-based prognostics approaches capture system knowledge in the form of physics-based models of components that include how they fail. These methods consist of a damage estimation phase, in which the health state of a component is estimated, and a prediction phase, in which the health state is projected forward in time to determine end of life. However, the damage estimation problem is often multi-dimensional and computationally intensive. We propose a model decomposition approach adapted from the diagnosis community, called possible conflicts, in order to both improve the computational efficiency of damage estimation, and formulate a damage estimation approach that is inherently distributed. Local state estimates are combined into a global state estimate from which prediction is performed. Using a centrifugal pump as a case study, we perform a number of simulation- based experiments to demonstrate the approach.

How to Cite

Daigle, M. ., Bregon, A. ., & Roychoudhury, I. . (2011). Distributed Damage Estimation for Prognostics based on Structural Model Decomposition. Annual Conference of the PHM Society, 3(1). https://doi.org/10.36001/phmconf.2011.v3i1.2037
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Keywords

model-based prognostics, particle filters, distributed prognostics, centrifugal pump

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Section
Technical Papers

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