Empirical mode decomposition approach for defect detection in non-stationary thermal wave imaging

This paper introduces a novel empirical mode decomposition based anomaly detection in Quadratic frequency modulated thermal wave imaging. Being suited for non-stationary signal analysis, its edge over other contemporary processing modalities in its anomaly detection capability has been verified using experimentation carried over a mild steel specimen with embedded flat bottom holes. It also addresses the effect of size and depth on anomaly detection using the proposed methodology in addition to considering the signal to noise ratio of defects for detection.