Real-time and high-precision cracks inversion algorithm for ACFM based on GA-BP neural network

Key words:  alternating current field measurement(ACFM)  real-time  high-precision  cracks inversion algorithm  genetic algorithm  BP neural network

Abstract:

It is hard to achieve a real-time and high-precision cracks inversion for alternating current field measurement(ACFM) based on traditional characteristic signals. In this paper, based on the finite element method (FEM) model of electromagnetic coupling ACFM probe, the energy spectrum and phase threshold determination methods were presented to obtain the crack characteristic signals in real time. The real-time and high-precision cracks inversion system for ACFM was set up and verified by artificial cracks experiment. The length and depth of cracks were calculated using the characteristic signals obtained from experiments based on the genetic algorithm and back propagation neural network(GA-BP) real-time and high-precision cracks inversion algorithm. The results show that the FEM model of electromagnetic coupling ACFM probe can simulate the characteristic signals perfectively, the energy spectrum and phase threshold determination method can obtain the crack characteristic signals in real time, the GA-BP neural network can realize the inversion of the length and depth of crack perfectly and the relative error of inversion accuracy is less than 10％.