Inverse mapping of magnetic flux leakage signal for defect characterization

Abstract

Estimation of shape and size of metal loss defects in ferromagnetic plates/pipes from magnetic flux leakage (MFL) signals calls for the development of an inverse mapping system, which is very often non-linear and ill-posed. Identification of such systems needs regularization and modeling in multi-resolution framework. This paper proposes a new inverse mapping approach for defect profile estimation and sizing from MFL signals, using a partially time-varying model. The model works with wavelet projections and selectively employs time varying and time invariant models in different sub-bands to estimate the defect shape. Shape synthesis uses alternate projections (in two signal spaces) that converge to minimum norm solution. Length and width profile of defects are estimated independently. The model is trained with reference defects and is validated with unknown signals from laboratory and field runs in test rigs. This paper reports 1-D results which show excellent match with actual sizes and shapes of defects.