Application of Hilbert–Huang transform for defect recognition in pulsed eddy current testing

Abstract

Defect recognition plays an important role in the structure integrity and health monitor of in-service equipment. However, it is difficult to recognise deep-layer defect or small-size defect in conductive structure during pulsed eddy current (PEC) testing. Aiming at the issue, this article proposes a method based on Hilbert–Huang transform which consists of two modules: data processing and defect recognition. In the data processing module, the PEC response signal is decomposed into a few of intrinsic mode functions (IMFs) using ensemble empirical mode decomposition method. The IMFs whose variance contribution rates are bigger than 1% are chosen to reconstruct signal in order to remove noise. In the defect recognition module, the features based on specific frequency components of marginal spectrum (MS) of the reconstructed signals are extracted to discriminate those defects in surface and subsurface. Furthermore, the normalisation MS energy ratio is proposed to quantify defects which cannot be distinguished using peak value in time domain. Experiments show that the proposed method can achieve better de-noising effect and defect evaluation, which contributes to the recognition of those complicated defects such as deep-layered and small-sized defect.