The contents of this paper reflect the view of the authors who are solely responsible for the accuracy of the facts, analyses, conclusions, and recommendations presented herein, and do not necessarily reflect the official view or policy of the Federal Aviation Administration. The issue of aircraft safety is a major concern for the Federal Aviation Administration, the aviation industry, and the general public. 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 forecasted values of the model were then compared to the actual values using statistical analysis. Each airline operator and the FAA have an obligation to ensure the safety of each passenger aboard each aircraft.