The Impact of Metrics on the Choice of Prognostic Methodologies

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.