Detection and evaluation of impact damage on aircraft control surface using acoustic emission and convolution neural network

Impact damage is one of the major threats to the integrity of aircraft control surfaces such as wings and elevators. The conventional and widely applied inspection approach is visual inspection which is time-consuming and subject to human error. The innovation of this paper lies in developing a smart sensing system by leveraging acoustic emission (AE) for the real-time detection and evaluation of impact damage on aircraft elevators. The challenge of this system is to deploy a minimal number of AE sensors on the aircraft due to the environmental restriction during the operation of the aircraft while still effectively evaluate the impact damage. A convolutional neural network (CNN) is employed to localize the impact and evaluate the damage level by analyzing the wavelet of signals obtained by a single AE sensor. The proposed approach is verified by an impact test carried out on a thermoplastic aircraft elevator. The results demonstrate the efficacy and potential of the proposed approach.