Piezoelectric sensors are increasingly being used in active structural health monitoring, due to their durability, light weight and low power consumption. In the present work damage detection and characterization methodologies based on Lamb waves have been evaluated for aircraft panels. The applicability of various proposed delay-and-sum algorithms on isotropic and composite stiffened panels have been investigated, both numerically and experimentally. A numerical model for ultrasonic wave propagation in composite laminates is proposed and compared to signals recorded from experiments. A modified delay-and-sum algorithm is then proposed for detecting impact damage in composite plates with and without a stiffener which is shown to capture and localize damage with only four transducers.

In this work a methodology for impact identification on composite stiffened panels using piezoceramic sensors has been presented. A large number of impacts covering a wide range of energies (corresponding to small and large mass impacts) at various locations of a composite stiffened panel have been simulated using the finite element (FE) method. To predict the impact location, artificial neural networks have been established using the data generated from FE analyses. A number of sensor signal features have been examined as inputs to the neural network and the effect of noise on the predictions has been investigated. The results of the study show that the trained network is capable of locating impacts with different energies at different locations (e.g. in the bay, over/under the stringer and on the foot of the stringer) in a complicated structure such as a composite stiffened panel.

Impacts can inflict critical consequences on composite structures, therefore smart impact monitoring systems can be very helpful. A global optimization of the piezoelectric (PZT) sensors position for impact location identification is investigated in this paper. Artificial Neural Networks (ANNs) and probabilistic analysis approach are used to define the objective function. Genetic algorithms (GAs) are adopted to search for the optimal location of the sensors. Improved crossover and mutation functions are designed. The procedure is applied to a full-scale stiffened composite aircraft panel

In this work, a new methodology is presented for reconstructing the impact force history using Artificial Neural Networks (ANNs) and spectral components of sensor data recorded by piezoceramic sensors. A large set of data, required for training the ANNs, were generated by using an efficient nonlinear Finite Element (FE) model of a sensorised composite stiffened panel. Impact experiments were performed on a composite plate equipped with surface-mounted piezoceramic sensors to validate the numerical modelling approach. Using the FE model of the panel, data were generated for impacts which are likely to occure during life-time of an aircraft, containing large mass (e.g. dropping tool) and small mass (e.g. debris) impacts at various locations, i.e. in bay, on the foot of stringer and over/under stringer. Even though the panel undergoes large deformation during impact (nonlinear response), the established networks predict the impact force history and its peak with reasonable accuracy.

This paper reports on a novel metamodel for impact detection, localization and characterization of complex composite structures based on Convolutional Neural Networks (CNN) and passive sensing. Methods to generate appropriate input datasets and network architectures for impact localization and characterization were proposed, investigated and optimized. The ultrasonic waves generated by external impact events and recorded by piezoelectric sensors are transferred to 2D images which are used for impact detection and characterization. The accuracy of the detection was tested on a composite fuselage panel which was shown to be over 94%. In addition, the scalability of this metamodelling technique has been investigated by training the CNN metamodels with the data from part of the stiffened panel and testing the performance on other sections with similar geometry. Impacts were detected with an accuracy of over 95%. Impact energy levels were also successfully categorized while trained at coupon level and applied to sub-components with greater complexity. These results validated the applicability of the proposed CNN-based metamodel to real-life application such as composite aircraft parts.