Neural network forecasting of equipment inoperability for an aircraft operator

Abstract

The issue of aircraft safety is a major concern for the Federal Aviation Administration, the aviation industry, and the general public. In this research report, the problem of aircraft safety is dealt with by focusing on the Service Difficulty Report (SDR). SDRs are completed for each instance of equipment inoperability and an SDR often precedes a critical safety problem. The purpose of this research effort is to forecast the monthly number of SDRs for an aircraft operator.

The method used to create the SDR forecasting model is a neural network. Neural networks are extremely useful in detecting underlying trends in data. Data from the Federal Aviation Administration and Department of Defense databases were used for the forecasting model. An important feature of this research effort is that the trending of SDRs was done using a mixed fleet composition of data, which in the past has not given favorable results. Next, several different architectures for the neural network model were implemented and analyzed. The General Regression Neural Network (GRNN) architecture was found to be the best. The forecasted values of the model were then compared to the actual values using statistical analysis. For the GRNN model, the coefficient of multiple determination, R2, was found to be 0.8962 which indicates that a good SDR forecasting model has been found for the operator.