Abstract
Pipeline-safety evaluation is an important problem for industry. On the basis of the magnetic-flux-leakage (MFL) method, this paper presents an automated inspection device for inspection of pipeline defects, analyzes the MFL-inspection theory and some defect-feature parameters, and gives a recognizing algorithm based on a dynamic wavelet-basis-function (WBF) neural network. This dynamic network utilizes a multiscale and multiresolution orthogonal wavelet and backward-propagating through signals and has more significant advantages than BP or other neural networks used in MFL inspection. It also can control the accuracy of the predicted defect profiles, possessing high-speed convergence and good approaching features. The performance that applies the algorithm based on the network for predicting a defect profile from experimental MFL signals is also presented.
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