Indirect aircraft structural monitoring using artificial neural networks

摘要

From necessity, military aircraft often operate in a highly fatigue damaging environment and history has shown in lost lives and aircraft the consequences of failure to appreciate fully the usage environment. The need for robust and cost effective structural usage monitoring of military aircraft to ensure operations are conducted within acceptable levels of risk is paramount. Furthermore, increased economic pressures require ever-inventive methods to be employed to maximise the lives of military fleets; structural usage monitoring will be a key asset in this drive. A highly cost effective indirect structural health and usage neural network (SHAUNN) monitoring system is proposed. A SHAUNN uses regression relationships determined by artificial neural networks to predict stresses, strains, loads, or fatigue damage from flight parameters. Within this paper the development of a SHAUNN monitoring system is described. Flight parametric data, captured during Operational Loads Measurement of the Royal Air Force Dominie TMk1 aircraft have been used to predict stresses at the key structural location in the wing, using mapping relationships determined by artificial neural networks. A framework for the development of the SHAUNN monitoring system is discussed and the basic architecture of the multilayer perceptron artificial neural network is described. It is concluded that this technology could provide the basis for an accurate, cost-effective structural usage monitoring system and further work to investigate the prediction of ground -based stresses in the wing is recommended.