Ultrasonic feature imaging of a multi-layered structure beyond a thin, highly reflective layer using a convolutional neural network

Abstract

A helicopter propeller is a kind of multi-layered composite material bonding structure. Ensuring that composite structures are free from defects can reduce the risk of in-service failure and hence improve safety. As a common non-destructive testing (NDT) technology, ultrasonic testing is often used in the inspection of composite structures. However, a composite structure made of multiple thin-layer materials bonded together can cause a serious aliasing problem for echo signals when inspecting with ultrasound. In this study, the frequency-domain characteristics of an aliasing echo signal were analysed using the spectrum of the acoustic pressure reflection coefficient. Furthermore, the time-frequency joint analysis results of the echo signal were obtained using a continuous wavelet transform. Finally, the obtained time-frequency features of the echo signal were used to classify and image with a convolutional neural network (CNN). The results revealed that, as opposed to the direct imaging of the time- and frequency-domain features, the time-frequency wavelet map of a thin-walled multi-layered structure that was classified and imaged with a CNN exhibited greater clarity and better defect recognition ability. In addition, the training time of the CNN was 17 s and the classification accuracy of the verification set was high, reaching 97.8%.