Machine Learning-Based Corrosion-Like Defect Estimation With Shear-Horizontal Guided Waves Improved by Mode Separation

Mateus Gheorghe de Castro Ribeiro, Helon Vicente Hultmann Ayala

Abstract:

Shear Horizontal (SH) guided waves have been extensively used to estimate and detect
defects in structures like plates and pipes. Depending on the frequency and plate thickness, more than
one guided-wave mode propagates, which renders signal interpretation complicated due to mode mixing
and complex behavior of each individual mode interacting with defects. This paper investigates the use of
machine learning models to analyse the two lowest order SH guided modes, for quantitative size estimation
and detection of corrosion-like defects in aluminium plates. The main contribution of the present work is
to show that mode separation through machine learning improves the effectiveness of predictive models.
Numerical simulations have been performed to generate time series for creating the estimators, while
experimental data have been used to validate them. We show that a full mode separation scheme decreased
the error rate of the final model by 30% and 67% in defect size estimation and detection respectively.