[Objective]The field signature method(FSM)corrosion monitoring experiment is a crucial component of the Non-destructive testing(NDT)technology course system's teaching reform.However,the high cost of FSM equipment limits hands-on opportunities for students.To overcome this problem and address the challenges associated with small pitting resolution,we propose a new approach.This paper presents a straight pipe pitting corrosion positioning and parameter inversion algorithm using neural networks and multiple linear regression.To help students grasp the fundamental principles of FSM corrosion monitoring and understand FC signal distribution under various corrosion parameters,we integrated numerical simulation and virtual experimental teaching into the curriculum.[Methods]During slow corrosion processes,real-time electric field distribution measurement across different pitting parameters is difficult.We used numerical simulations to calculate these electric field signals in a pipeline model with simulated pitting.We assessed how different pitting parameters affect the electric field distribution to establish a database for FSM.The relationship between pitting morphology parameters and FC values was analyzed,providing a method for determining pitting locations.A neural network-based correction method was proposed to eliminate the changes in the electric field distribution caused by different pitting positions.To improve resolution accuracy for small corrosion pits,we analyzed FC value changes caused by defects under different pitting parameters.We developed analytical equations using multiple linear regression to enable precise inversion of pitting morphology parameters.In the meantime,a virtual FSM experimental system was designed using MATLAB App based on numerical simulations and the pipeline pitting inversion algorithm.[Results]The virtual simulation platform for corrosion monitoring yielded the following outcomes:1)An FSM-based pipeline simulation method was developed,clarifying electric field distributions under different parameters.2)Methods for locating and correcting pitting corrosion positions were constructed,along with an inversion algorithm for pitting corrosion morphology parameters,enabling effective tracking of pitting corrosion.3)A virtual FSM experiment was devised,addressing the inability of the traditional FSM corrosion monitoring experiment to provide adequate hands-on experience for each student.[Conclusions]Virtual simulation experimental teaching practices demonstrate that numerical simulation can help explain and understand FSM corrosion monitoring.This approach compensates for the deficiencies of traditional corrosion monitoring experimental courses,stimulates students'interest in corrosion monitoring,and cultivates an innovative spirit among learners.
维普
万方数据