Feature extraction and neural network-based fatigue damage detection and classification

Abstract

This paper proposes a methodology for detection and classification of fatigue damage in mechanical structures in the framework of neural networks (NN). The proposed methodology has been tested and validated with polycrystalline-alloy (AL7075-T6) specimens on a laboratory-scale experimental apparatus. Signal processing tools (e.g., discrete wavelet transform and Hilbert transform) have been applied on time series of ultrasonic test signals to extract features that are derived from: (i) Signal envelope, (ii) Low-frequency and high-frequency signal spectra, and (iii) Signal energy. The performance of the neural network, combined with each one of these features, is compared with the ground truth, generated from the original ultrasonic test signals and microscope images. The results show that the NN model, combined with the signal-energy feature, yields the best performance and that it is capable of detecting and classifying the fatigue damage with (up to) 98.5% accuracy.