A Hyper-solution Framework for SVM Classification: Improving Damage Detection on Helicopter Fuselage Panels

The on-line assessment of structural health of aircraft fuselage panels and their remaining useful life is crucial both in military and civilian settings. This paper presents an application of a Support Vector Machines (SVM) classification framework aimed at improving the diagnosis task based on the strain values acquired through a monitoring sensor network deployed on the helicopter fuselage panels. More in details, diagnosis is usually defined as detecting a damage, identifying the specific component affected (i.e., bay or stringer) and then characterizing the damage in terms of center and size. Here, the first two steps are performed through the SVM classification framework while the last one is based on an Artificial Neural Network (ANN) hierarchy already presented in a previous authors’ work.The training dataset was built through Finite Elements Method (FEM) based simulation, able to simulate the behavior of any type of panel and damage according to specific parameters to set up; the result of FE simulation consists of the strain fields on different locations. As results, the proposed SVM classification framework permits to improve reliability of detection and characterization tasks respect to the previous approach entirely based on ANN hierarchies.Finally, the remaining useful life is estimated by using another ANN, different for damage on bay and stringer, able to predict the values of two parameters of the NASGRO equation which is used to estimate the damage propagation.