Model-based Prognostics with Fixed-lag Particle Filters

Matthew Daigle and Kai Goebel
Submission Type: 
Full Paper
AttachmentSizeTimestamp
phmc_09_37.pdf595.03 KBSeptember 17, 2009 - 8:16am

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.

Publication Control Number: 
037
Submission Keywords: 
applications: space
filtering
model based prognostics
particle filtering
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