Health Monitoring of an Auxiliary Power Unit Using a Classification Tree
The objective of this work is to present a method to monitor the health of Auxiliary Power Units (APU) using a Dynamic Computational Model, Gas Path Analysis and Classification and Regression Trees (CART). The main data used to train the CART consists of measurements of the exhaust gas temperature, the bleed pressure and the fuel flow. The proposed method was tested using actual APU data collected from a prototype aircraft. The method succeeded in classifying several relevant fault conditions. The few misclassification errors were found to be due to the insufficiency of the information content of the measurement data. *
STATISTICAL MODELING OF AIRCRAFT ENGINE FUEL FLOW RATE
Knowledge of the fuel flow rate is important for accurate fuel burn and emissions inventory generation and for accurate engine performance modeling. In this paper, machine learning techniques are applied to aircraft flight recorder data to model the fuel flow rate as a function of the aircraft altitude, ground speed, vertical speed, and takeoff mass in the airborne phases of flight. Models are built using the Classification and Regression Trees (CART) and the Least Squares Boosting algorithms. The models are found to be interpretable. They are also shown to predict fuel flow rate and its variability more accurately than other frequently-used methods for fuel flow rate estimation. A sensitivity analysis of the predicted fuel flow rate to takeoff mass is also conducted. The proposed nonparametric models can help set a benchmark for developing more sophisticated data-driven statistical models of engine fuel flow rate.
Testing the Core Empirical Implications of Gottfredson and Hirschi's General Theory of Crime
In A General Theory of Crime, Gottfredson and Hirschi propose that low self-control, in interaction with criminal opportunity, is the major cause of crime. The research reported in this article attempts to test this argument while closely following the nominal definitions presented by Gottfredson and Hirschi. A factor analysis of items designed to measure low self-control is consistent with their contention that the trait is unidimensional. Further, the proposed interaction effect is found for self-reported acts of both fraud and force (their definition of crime). Inconsistent with the theory are (a) the finding that criminal opportunity has a significant main effect, beyond its interaction with low self-control, on self-reported crime and (b) the substantial proportion of variance in crime left unexplained by the theoretical variables. Suggestions are offered for modifying and expanding the theory.