Using neural networks to predict component inspection requirements for aging aircraft

Currently under development by the Federal Aviation Administration (FAA), the Safety Performance Analysis System (SPAS) will contain indicators of aircraft safety performance that can identify potential problem areas for inspectors. The Service Difficulty Reporting (SDR) system is one data source for SPAS and contains data related to the identification of abnormal, potentially unsafe conditions in aircraft or aircraft components/equipment. A higher expected number of SDRs suggests a greater possibility of a maintenance problem and may be used to alert Aviation Safety Inspectors (ASIs) of the need for preemptive safety or repair actions. The preliminary SDR performance indicator in SPAS is not well defined and is too general to be of practical value. In this study, an artificial neural network model is created to predict the number of SDRs that could be expected by part location using sample data from the SDR database that have been merged with aircraft utilization data. The predictions from the neural network models are then compared with results from multiple regression models. The methodological comparison suggests that artificial neural networks offer a promising technology in predicting component inspection requirements for aging aircraft.