AUTOMATIC ASSESSMENT OF THE SEVERITY OF CRACKS IN STEEL TUBES USING NEURAL NETWORKS

This paper describes work on an extension of a previous neural network classifier ((1)) that is able to perform an automatic assessment of t he severity of cracks in the walls of a steel tube through the detecti on of the leaked magnetic field. In the present report, the authors an alyse the performance of this neural network for realistic conditions in the measuring process. This is modelled with a population of signal s in which both the shape of the cracks and other physical parameters that are inherent to the detection process are allowed to change rando mly. The neural network classifier is able both to assess the potentia l danger of the crack and determine its location in the internal or ex ternal wall of the tube. The neural network behaves as a robust classi fier-showing in all cases a significant improvement over the tradition al method of estimating the potential danger of the crack through the amplitude of the detected signal.