Model-based Prognostics with Fixed-lag Particle Filters

Shared by Matthew Daigle, updated on Feb 07, 2012


Author(s) :
Matthew Daigle, Kai Goebel

Model-based prognostics exploits domain knowledge of the system, its components, and how they fail by casting the underlying physical phenomena in a physics-based model that is derived from first principles. In most applications, uncertainties from a number of sources cause the predictions to be inaccurate and imprecise even with accurate models. Therefore, algorithms are employed that help in managing these uncertainties. Particle filters have become a popular choice to solve this problem due to their wide applicability and ease of implementation. We present a general model-based prognostics methodology using particle filters. In order to provide more accurate and precise estimates, and, therefore, more accurate and precise predictions, we investigate the use of fixed-lag filters. We develop a detailed physics-based model of a pneumatic valve, and perform comprehensive simulation experiments to illustrate our prognostics approach. The experiments demonstrate the advantages that fixed-lag filters may provide in the context of prognostics, as measured by prognostics performance metrics.

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Publication Name
Proceedings of the Annual Conference of the Prognostics and Health Management Society 2009
Publication Location
San Diego, CA
Year Published



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