Aircraft fly all over the world and are hardly stationed at one sole location. Whether commercial or military aircraft, the survivability of expensive parts play a major role on setting up contracts and determining requirements. Though certain parts share similar characteristic, location characteristics play the major role such as maintenance concepts, mission types, and climate where operated.
This thesis explores how twenty-three locations around the world impact the survivability of 1,500+ unique aircraft parts in the past five years. Cluster analysis is used to group these 1,500+ parts in terms of failures to airframe parts, avionic parts, propulsion parts, lower assemblies, higher assemblies, and criticality due to non-mission capability. Different causals are explored: flight hours, landings, stops and the number of aircrafts utilizing the parts to obtain the best model. Gamma regression vs. different optimal lambdas with box-cox transformations are applied to the eight clusters to identify the best model based on smallest mean square error. A decision tree based on unit cost split out into quartiles of equally dense break out is applied to identify the best factors to use for optimization through marginal analysis. Rstudio is used for statistical computing.
|Commitee:||Zhou, Tianni, Suaray, Kagba|
|School:||California State University, Long Beach|
|Department:||Mathematics and Statistics|
|School Location:||United States -- California|
|Source:||MAI 81/1(E), Masters Abstracts International|
|Subjects:||Aerospace engineering, Engineering|
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