Model-based prognostics exploits domain knowl- edge of the system, its components, and how they fail by casting the underlying physical phenom- ena in a physics-based model that is derived from first principles. In most applications, uncertain- ties from a number of sources cause the predic- tions to be inaccurate and imprecise even with accurate models. Therefore, algorithms are em- ployed 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 gen- eral model-based prognostics methodology using particle filters. In order to provide more accu- rate 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 exper- iments demonstrate the advantages that fixed-lag filters may provide in the context of prognostics, as measured by prognostics performance metrics.