Pipeline defects diagnosis based on feature extraction and neural-network fusion

Abstract

Corrosion, stresses, and mechanical damage of oil and gas pipelines can result in catastrophic failures. So, pipeline-safety nondestructive evaluation is a research hotspot. A pipeline-defect diagnosis approach is proposed to classify detected defects into correct classes that are useful for later defect assessment and repair, effect raw signals are processed by the independent component analysis method, and several independent components are obtained. The residual mutual information matrixes are then constructed. These matrixes that contain useful high-order statistical information are used to train a radial-basis-function fusion network. Laboratory experiments have verified the effectiveness and practicability of this approach. The classification accuracy can reach 92.5%.