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<p>This thesis provides a stochastic modeling tool to assist in the component selection process for Army Aviation's Condition-Based Maintenance Plus (CBM+) program. This work is in conjunction with the Operations Research Center of Excellence (ORCEN) at the United States Military Academy to assist in providing insight for the U.S. Aviation and Missile Command (AMCOM).  The component selected for this thesis is the AH-64/UH-60 T701C Turbine Helicopter Engine. Data analysis of the failure data indicated that a nonhomogeneous Poisson process appropriately modeled the failure characteristics of this engine. A Microsoft Excel simulation utilizing Crystal Ball version 5.5 compares an engine monitored by CBM+ versus the traditional Legacy system of maintenance. This simulation provides information on diagnosed faults, mission aborts, repair times, false positives, and logistical implications.  This simulation is generic and can be used in comparing CBM+ candidate components for future inclusion into the CBM+ program. Results suggest when considering a component for inclusion in the CBM+ program important factors to consider are even the smallest false positive rate can invalidate process, large sensor probability of detection isn't necessary for beneficial results, and by entering a component into the CBM+ the on hand component requirements can be greatly reduced.
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<br>Subjects: Decision making; Stochastic processes</div>
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Gauthier, Stephen E.</div>
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