Experiments in Bayesian Diagnostics with IUID-Enabled Data

The Department of Defense (DOD) has recognized the importance of improving asset management and has created Item Unique Identification numbers (lUIDs) to improve the situation. lUIDs will be used to track financial and contract records and obtain location and status information about parts in DoD inventory. lUIDs will also support data collection for weapon systems from build, test, operations, maintenance, repair, and overhaul histories. In addition to improving the overall logistics process, lUIDs offer an opportunity to utilize asset-specific data to improve system maintenance and support. An Office of the Secretary of Defense (OSD) Pilot Project to implement IUID on a Navy weapon system presents an immediate opportunity to evaluate this use of IUID data. This paper reports on experiments conducted to see if a set of asset-specific diagnostic classifiers trained on subsets of data is more accurate than a general, composite classifier trained on all of the data. In general, it is determined that the set is more accurate than the single classifier given enough data. However, other factors play an important role such as system complexity and noise levels in the data. Additionally, the improvements found do not arise until larger amounts of data are available. This suggests that future work should concentrate on tying the process of data collection to the estimation of the associated probabilities.