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

来源：

PROCEEDINGS OF THE 2006 WINTER SIMULATION CONFERENCE, VOLS 1-5 | 2006年

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

页码：1253 / +

页数：3

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