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Type of Document	Master's Thesis
Author	Cox, Alan R, Jr.
URN	etd-07202005-122009
Title	Predicting Helicopter Faults by Analyzing the Stability of Vibration Time Series
Degree	Master of Science
Graduate Program	Operations Research
Advisory Committee	Advisor Name Title Thom Hodgson Committee Chair LTC Michael J. Kwinn, Jr. Committee Member Ralph Smith Committee Member Subhashis Ghosal Committee Member
Keywords	Classification Trees Classification Helicopters Failures Time Series Explosive Stability Vibration Neural Networks Neural Networks
Date of Defense	2005-06-29
Availability	unrestricted
Abstract The U.S. Army's Lead-the-Fleet (LTF) program was started to help the Army develop a better maintenance program for its helicopters. This thesis explores vibration data gathered from the U.S. Army?s and the South Carolina National Guard's Vibration Management Enhancement Program (VMEP). Vibration time series are classified as either "explosive" or "stationary." This classification is then used by neural networks and classification trees to predict whether a part failed directly after a flight and is need of replacement. The belief is that this will give the maintenance personnel a better understanding of when parts fail, allowing for a more accurate replacement schedule that could save money and improve safety.
Files	Filename       Size       Approximate Download Time (Hours:Minutes:Seconds)     28.8 Modem   56K Modem   ISDN (64 Kb)   ISDN (128 Kb)   Higher-speed Access    etd.pdf 4.44 Mb 00:20:32 00:10:33 00:09:14 00:04:37 00:00:23