Predicting Helicopter Faults by Analyzing the Stability of Vibration Time Series
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Date
2005-07-20
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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.
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Keywords
Classification Trees, Classification, Helicopters, Failures, Time Series, Explosive, Stability, Vibration, Neural Networks, Neural, Networks
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Degree
MS
Discipline
Operations Research
