Application of artificial neural networks for compounding multiple damage indices in Lamb-wave-based damage detection

This paper presents a novel approach to the problem of health monitoring of aircraft structures using Lamb waves. Piezoelectric sensors, embedded in the aircraft sheathing, generate Lamb waves with the aim to monitor the structural integrity of complex structure parts. The ultrasonic signals obtained from the sensor pairs arranged in pitch-catch configuration are used for the calculation of a number of different damage indices. The damage indices are then used as inputs for a classifier employing an artificial neural network (ANN) that is trained to perform structure condition assessment. Efficiency of the ANN classifier trained on artificial data generated from the numerical simulations performed using linear interaction simulation approach is investigated. The resulting classification results are compared with those obtained for the ANN trained on experimental data from the real specimens.