Identification of Defects in Pipelines Through a Combination of FEM and ANN

Abstract

Defects identification method in the pipeline system is proposed. The method is based on a combination of finite element method (FEM) and artificial neural networks (ANNs). A finite element modeling of the monitoring system of the damaged state of the pipeline, which is a fragment of a pipe with a defect and piezoelectric actuators and sensors is carried out. The direct problem is reduced to initial boundary value problem of the theory of elasticity and electrodynamics. The inverse problem of identification of defects is reduced to the inverse geometrical problem. As additional information for the solution of inverse problems is the amplitude–time response (ATR) of electric potential on the free electrode sensors, the sensors were located before and after a defect, for measuring the reflected and transmitted acoustic waves excited by the actuators. Using this model, a set of direct problems is solved and a training set for the ANN is constructed. As the ANN architecture, we select a multilayer perceptron and back propagation learning algorithm is considered. The algorithm for the identification of defects contains several steps: (i) the location of a defect (determining the distance between the actuators and sensors and defect); (ii) determining the type of the defect (crack, volumetric defect); and (iii) the determination of the defect parameters (depth, slope of the crack , geometric parameters of the volume defect). A series of numerical experiments, in which the optimal ANN architecture, defined for each identification step, is performed.