Characterization of gas pipeline inspection signals using wavelet basis function neural networks

Abstract

Magnetic flux leakage techniques are used extensively to detect and characterize defects in natural gas transmission pipelines. This paper presents a novel approach for training a multiresolution, hierarchical wavelet basis function (WBF) neural network for the three-dimensional characterization of defects from magnetic flux leakage signals. Gaussian radial basis functions and Mexican hat wavelet frames are used as scaling functions and wavelets respectively. The centers of the basis functions are calculated using a dyadic expansion scheme and a k-means clustering algorithm. The results indicate that significant advantages over other neural network based defect characterization schemes could be obtained, in that the accuracy of the predicted defect profile can be controlled by the resolution of the network. The feasibility of employing a WBF neural network is demonstrated by predicting defect profiles from both simulation data and experimental magnetic flux leakage signals.