KPLS-RWBFNN model for MFL 2D defect profile reconstruction

Abstract

Kernel partial least squares (KPLS) is normally very efficient for tackling nonlinear systems by mapping an original input space into a high-dimensional feature space and creating a linear PLS model in the feature space. Unlike other nonlinear PLS techniques, KPLS does not entail any nonlinear optimisation procedures. However, due to the linear inner model of PLS, KPLS is still inappropriate for describing the significant nonlinear characteristic data structure while dealing with complex physical systems in practical situations. Under this circumstance, radial wavelet basic function neural network (RWBFNN) can replace the linear inner model of PLS in the nonlinear kernel-based algorithm. Thus, KPLS-RWBFNN model is proposed in this paper and applied to multi-resolution approximation reconstruction of 2D defect profiles in magnetic flux leakage testing. The reconstructions of 2D defect profiles by this method are implemented, and the comparisons among reconstructions by KPLS, RWBFNN and the proposed approach are also undertaken. Meanwhile, the reconstructions of 2D defects by RWBFNN and the proposed approach at different SNR are also executed. The results indicate that KPLS-RWBFNN model could simplify the structure of the network while holding well-behaved generalisation and multi-resolution approximation and predict the 2D defect profiles accurately and rapidly with good robustness.