A fast modeling method of pulsed eddy current with double-coil based on model-driven BP neural network

Abstract

ABSTRACT The analytical model serves as an important guiding role in practical testing. Currently, there is scarce research on the building of the analytical model for pulsed eddy current (PEC) with double-coil excited by a voltage source. This paper derives a frequency-domain model for PEC from a double-coil’s equivalent circuit. To tackle low modeling efficiency due to complex impedance changes and mutual inductance changes, a BP neural network (BPNN) maps lift-off, thickness, and frequency to impedance changes and mutual inductance changes. Training samples for the BPNN come from analytical models of impedance change and mutual inductance change. The study finds that a BPNN with four hidden layers and Bayesian regularization significantly improves prediction accuracy. Finally, the correctness of the BPNN method is verified. The PEC signals obtained by the BPNN method, the analytical model, and the Comsol finite element method are compared under different thicknesses and lift-offs, and it is found that the consistency of the PEC signals of these three methods is very well. And, the amplitudes of the residual signals between the BPNN method and the analytical model of the PEC signals are relatively small, and the relative errors at the peaks are no more than 0.4%.