Abstract
This paper presents an approach to damage growth monitoring and early damage detection in the structure of PZL - 130 ORLIK TC II turbo-prop military trainer aft using the statistical models elaborated by the Polish Air Force Institute of Technology (AFIT) and the network of the sensors attached to the structure. Drawing on the previous experiences of the AFIT and AGH in structural health monitoring, the present research will deploy an array of the PZT sensors in the structure of the PZL -130 Orlik TC II aircraft. The aircraft has just started Full Scale Fatigue Test (FSFT) that will continue up to 2013. The FSFT of the structure is necessary as a consequence of the structure modification and the change of the maintenance system - the transition to Condition Based Maintenance. In this paper, a novel approach to the monitoring of the aircraft hot-spots will be presented. Special attention will be paid to the preliminary results of the statistical models that provide an automated tool to infer about the presence of damage and its size. In particular, the effectiveness of the selected signal characteristics will be assessed using dimensional reduction methods (PCA) and the so-called averaged damage indices will be delivered. Moreover, the results of the signal classification based on the neural network will be presented alongside the numerical model of the wave propagation. The work contains selected information about the project scope and the results achieved at the preliminary stage of the project