Abstract
The ultrasonic and magnetic flow inspection methods of identification of crack-like defects in coated pipes are proposed. The identification of inner defects was performed using ultrasonic methods and an artificial neural network approach. Model of a pipe cross-section with an inner coating and the propagation of an ultrasonic wave were developed. A cross-section of the pipe, reinforced by inner annular coat, and the magnetic field propagation in permanent magnets were modeled. The identification of several geometric parameters of defects was carried out. The influence of different geometric parameters of the defects on the performance of neural networks is investigated. The optimal structure of the neural networks and the form of training vectors for methods of ultrasonic testing and magnetic flow inspection are established.
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