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

Authors:
Michael K. Painter;Madhav Erraguntla;Gary L. Hogg, Jr.;Brian Beachkofski
Affiliations:
Knowledge Based Systems, Inc., College Station, TX;Knowledge Based Systems, Inc., College Station, TX;Knowledge Based Systems, Inc., College Station, TX;Air Force Research Laboratory (AFRL/PRTS), Wright-Patterson AFB, OH
Venue:
Proceedings of the 38th conference on Winter simulation
Year:
2006

Citing 2
Cited 5

An architecture of a knowledge-based simulation engine

WSC '94 Proceedings of the 26th conference on Winter simulation
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning

Optimized maintenance design for manufacturing performance improvement using simulation

Proceedings of the 40th Conference on Winter Simulation
Analyzing production modifications of a C-130 engine repair facility using simulation

Winter Simulation Conference
Simulation-based data mining solution to the structure of water surrounding proteins

IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Mining simulation data by rule induction to determine critical source areas of stream water pollution by herbicides

Computers and Electronics in Agriculture
Simulation data mining for supporting bridge design

AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.