Identification of Crack-like Defects Based on Methods of Magnetic Inspection and Neural Networks Technologies
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چکیده
The method of identification of cracks-like defects in coated pipes is proposed. A cross section of the pipe, reinforced by inner annular coat and the magnetic field propagation of permanent magnets modelled. The identification of several geometric parameters of defects is carried out. Investigated the influence of different geometric parameters of the defects on the performance of neural networks training. The optimal structure of the neural network and the form of training vectors for method of magnetic inspection are established.
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تاریخ انتشار 2017