Pipe failure prediction model based on periodic pipe wall thickness measurement using electromagnetic acoustic resonance

This study proposes a numerical model to predict pipe failure based on periodic non-destructive inspections using electromagnetic acoustic resonance (EMAR). A total of 149 artificially corroded samples that simulated flow-assisted corrosion were prepared; 27 of them were plates, and others were from pipes of various dimensions. The samples were measured using EMAR, and the thicknesses of the samples were evaluated based on the fundamental resonance frequency. The results of the evaluations were compared with the actual thicknesses measured by a caliper gauge to quantify the uncertainty in pipe wall thickness evaluation. Numerical evaluations were performed to predict pipe failure probability in the future based on the results of periodic EMAR measurements. Three scenarios of pipe thickness reduction were considered, and each scenario assumed that pipe thickness measurements by EMAR were performed every five years. The actual pipe wall thickness, which is not necessarily the same as the EMAR evaluations, is estimated as a probability density function using the Bayesian approach proposed in an earlier study by the authors. Whereas this study considers only pipe rupture, the results of numerical simulations support the validity of the model especially when the corrosion rate is not constant.