Artificial neural networks for non-destructive evaluation with ultrasonic waves in not accessible pipes

The design of non-destructive testing systems for fault detection in long and not accessible pipelines is an actual task in the industrial and civil environment. At this purpose the diagnosis based on the propagation of guided ultrasonic waves along the pipes offers an attractive solution for the fault identification and classification. The authors studied this problem by means of suitable artificial neural network models. Numerical techniques have been used to model different kinds of pipes and faults, and to obtain several returning echoes containing the fault information. These signals have been processed to filter the noise by using wavelets e blind separation methods and passed to a feature extractor system, whose purpose is to reduce the data dimensionality and to compute suitable features. The features selected from the signals have been further processed in order to limit the size of the neural network models without loss of information. At this purpose, the Garson's method and the principal component analysis have been investigated and compared. Finally, the extracted features have been used as input for the neural network models. In this paper, traditional feed-forward, multi layer perceptron networks have been used to classify position, width, and depth of the defects.