The Impact of Metrics on the Choice of Prognostic Methodologies
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Abstract
Metrics play an important part in the development and application of
prognostic methodologies as they provide the capability to characterize
and assess the quality of remaining useful life predictions. Although
there is a wide range of both, prognostic metrics and prognostic
methodologies available, the choice of those often is a demanding and
time consuming task. Additionally, they are often treated as two
separate problems to solve, while the choice of metrics has an impact on
the choice of prognostic methodology and vice versa. In this paper, we
therefore present a framework with the capability to automatically
choose prognostic settings given specific system data to account for
five different prognostic metrics. We then apply this framework to an
aircraft data set to characterize the impact of metrics on the choice of
prognostic methodologies. The results show that the choice of
optimization metric has a big impact on the output of the generic
prognostic framework and on the overall prognostic performance.