Using simulation, data mining, and knowledge discovery techniques for optimized aircraft engine fleet management

被引:24
|
作者
Painter, Michael K. [1 ]
Erraguntla, Madhav [1 ]
Hogg, Gary L., Jr. [1 ]
Beachkofski, Brian [2 ]
机构
[1] Knowledge Based Syst Inc, 1408 Univ Dr E, College Stn, TX 77840 USA
[2] US Air Force, Res Lab, AFRL PRTS, Wright Patterson AFB, OH 45433 USA
关键词
D O I
10.1109/WSC.2006.323221
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents an innovative methodology that combines simulation, data mining, and knowledge-based techniques to determine the near- and long-term impacts of candidate aircraft engine maintenance decisions, particularly in terms of life-cycle cost (LCC) and operational availability. Simulation output is subjected to data mining analysis to understand system behavior in terms of subsystem interactions and the factors influencing life-cycle metrics. The insights obtained through this exercise are then encapsulated as policies and guidelines supporting better life-cycle asset ownership decision-making.
引用
收藏
页码:1253 / +
页数:3
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