Model-based Prognostics with Fixed-lag Particle Filters



Published Mar 26, 2021
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.

How to Cite

Daigle, M. ., & Goebel , K. . (2021). Model-based Prognostics with Fixed-lag Particle Filters. Annual Conference of the PHM Society, 1(1). Retrieved from
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filtering, model based prognostics, particle filtering, applications: space

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