Reasoning logical rules from multisensor data for lifecycle management of aircraft structures

Maneuvering produces strain loading, which cumulates as fatigue damage to the structures of fighter jets. In order to manage the integrity of structures, critical structural details are monitored with specially designed sensor instrumentation. Monitoring aims at estimating remaining fatigue life of structures, i.e. producing knowledge on how much more fatigue damage each structural detail can undergo before it has to be repaired or changed. Fatigue life expenditure (FLE) caused by flying a maneuver can be estimated using available fatigue life calculation tools. The analyses concerning the usage of aircraft have shown that caused FLE depends not only on the type of the flight maneuver but also on the way the maneuver was flown. In other words, the dispersion of FLE values of nominally similar flight maneuvers can be very high. This paper considers the application of genetic programming (GP) to reasoning such logical rules from flight parameter signals that explain damaging of structural details during certain flight maneuvers. In other words, we are searching for easily interpretable logical rules with which it is possible to predict whether a flight maneuver instance causes minor or major damage to the structural detail in study. In the experiments, GP approach is compared to a tree classifier with the real flight monitoring data of the Finnish Air Force F-18 fleet. The rules reasoned by GP outperform the rules by the tree classifier in accuracy and simplicity. The knowledge deduced from the reasoned rules can be exploited in pilot training when learning to fly maneuvers in less damaging way.