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Computing the distance between point a and point b is typically considered to be very easy. However, there are times when computing a distance can take significant computation time; we call these expensive distance metrics. Suppose we have some expensive distance metric and we need to compute the distances between a bunch of points. This paper explores a method that to reduce the number of queries to the distance metric and the effect on clustering. The authors find that total run time can be reduced while only inducing small inaccuracies in clustering output.

The problem of performing general prognostics and health management, especially in electronic systems, continues to present significant challenges. The low availability of failure data, makes learning generalized models difficult, and constructing generalized models during the design phase often requires a level of understanding of the failure mechanism that elude the designers. In this paper, we present a new, generalized approach to PHM based on two commonly available probabilistic models, Bayesian Networks and Continuous-Time Bayesian Networks, and pose the PHM problem from the perspective of risk mitigation rather than failure prediction. We describe the tools and process for employing these tools in the hopes of motivating new ideas for investigating how best to advance PHM in the aerospace industry.

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