Fatigue damage detection of aerospace-grade aluminum alloys using feature-based and feature-less deep neural networks

Abstract

Fatigue damage is one of the most common causes of failure in aerospace structural components. While numerical modeling and laboratory-scale experimentation provide much insight to the physics of failure evolution, it is extremely challenging to account for all variabilies that a component may be subjected to during on-field operation. Human-supervised continuous monitoring of such components using sensors, therefore, provides a much-needed alternative for reliable operation of these components. By leveraging the concepts in deep learning, such human supervision can be assisted with an automated pre-trained deep network for damage detection. To that end, this article studies two distinct deep neural network (DNN) architectures for fatigue damage detection in aluminum using ultrasonic time-series data obtained from a novel customized fatigue testing apparatus. The first DNN, called as a feature-based network, is built by using two predefined features viz. frequency domain amplitude and autocorrelation from the ultrasonic data as the inputs. The second DNN, called as a feature-less network, uses the ultrasonic data as-is without any pre-processing and relies on the black-box features generated during training. The capability of fatigue crack detection for both DNN architectures is evaluated at two distinct stages of fatigue failure. The feature-less network is observed to outperform the feature-based network with an accuracy of 94.26% and 98.94% for the two stages. The result indicates that feature-less DNNs, owing to their construction, can formulate better, albeit black-box features, and simplify the process of choosing customized signal processing methods for similar problems.