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The Department of Defense (DOD) has recognized the importance of improving asset management and has created Item Unique Identification numbers (IUIDs) to improve the situation. IUIDs will be used to track financial and contract records and obtain location and status information about parts in DoD inventory. IUIDs 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.

The Institute for Electrical and Electronics Engineers (IEEE), through its Standards Coordinating Committee 20 (SCC20), is developing interface standards focusing on Automatic Test System-related elements in cooperation with a Department of Defense (DoD) initiative to define, demonstrate, and mandate such standards. One of these standards-IEEE Std 1232-2002 Artificial Intelligence Exchange and Service Tie to All Test Environments (AI-ESTATE)-has been chosen for demonstration prior to mandate. In this paper, we discuss the results of the first phase of the AI-ESTATE demonstration, focusing on semantic interoperability of diagnostic models. The results of this demonstration successfully showed the effectiveness of semantic modeling in information exchange. In addition, the engineering burden was demonstrated to be manageable: all applications were constructed in less than four months by three graduate students working part time. 1,2 TABLE OF CONTENTS 1. INTRODUCTION.

Abstract-In this paper we build upon previous work to examine the efficacy of blending probabilities in asset-specific classifiers to improve diagnostic accuracy for a fleet of assets. In previous work we also introduced the idea of using split probabilities. We add environmental differentiation to asset differentiation in the experiments and assume that data is acquired in the context of online health monitoring. We hypothesize that overall diagnostic accuracy will be increased with the blending approach relative to the single aggregate classifier or split probability assetspecific classifiers. The hypothesis is largely supported by the results. Future work will concentrate on improving the blending mechanism and working with small data sets.