Depth estimation of pipe wall thinning using multifrequency reflection coefficients of T(0,1) mode-guided waves with supervised multilayer perceptron

Abstract

This study entailed the development of a novel method for estimating the depth of wall thinning of pipes using multifrequency (30–65 kHz) reflection coefficients (MRCs) of the T(0,1) mode-guided waves and a multilayer perceptron (MLP). First, this study established why MRCs are a critical feature of the input layer of the MLP for the defect depth estimation of wall thinning. Further, a mathematical model that can quickly collect large amounts of training data was used to calculate the reflection waveforms. The depths of artificial and actual wall thinning were estimated using the MLP based on the MRCs and the mathematical model. Experiments were conducted using the T(0,1) mode-guided waves to obtain the MRCs for 21 artificial and 6 actual wall thinnings to estimate the defect depths. A maximum of 8347 training data points were prepared using the mathematical model. Because the optimization of the MLP strongly depended on the initial weights and biases, 100 random initial values were prepared to evaluate the average estimations and their standard deviations. The classification scheme of the MLP was used, with classification step widths of 0.5 and 0.25 mm. The correct answer rates for the 21 artificial defects were 93%, with a tolerance of ±0.5 mm for the 0.5 mm classification scheme; those for the 0.25 mm classification scheme were 89%. For the six actual defects, the correct answer rates were 100% with a tolerance of ±0.5 mm for both the 0.5 and 0.25 mm classification schemes. Sufficiently high correct answer rates were obtained in all the cases.