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Using commercial off-the-shelf business intelligence software tools to support aircraft and automated test system maintenance environments

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3 Author(s)
Head, S.C. ; Boeing Co., St. Louis, MO, USA ; Nielson, A.R. ; Au, M.-K.

The purpose of this paper is to provide information about the benefits using Commercial Off-the-Shelf (COTS) business intelligence software tools to support aircraft and automated test system maintenance environments. Aircraft and automated test system parametric and maintenance warehouse-based data can be shared and used for predictive data mining exploitation which will enable better decision support for War Fighters and back shop maintenance. When utilizing common industry business intelligence Predictive Modeling Processes, engineering designers can create initial business intelligence aircraft and automated test system maintenance environment engineering cluster models. This is a process of grouping together engineering data that have similar aggregate patterns. By using these engineering cluster models produced earlier to develop and build more accurate predictive models, predictive algorithms are utilized to make use of the cluster results to improve predictive accuracy. Common industry business intelligence Decision Trees and Neural Network models are developed to determine which algorithm produces the most accurate models (as measured by comparing predictions with actual values over the testing set). After an initial mining structure and mining model is built (specifying the input and predictable attributes), the analyst can easily add other mining models. COTS business intelligence software tools provide for a more cost effective support and predictive role for War Fighter support personnel in a time of decreased defense spending. Having access to applicable engineering data at the time of need will; decrease troubleshooting time on production aircraft and back shop maintenance, increase the ability of the technical user to better understand the diagnostics, reduce ambiguities which drive false removals of system components, decrease misallocated spares, and maintain/increase knowledge management.

Published in:

AUTOTESTCON, 2010 IEEE

Date of Conference:

13-16 Sept. 2010