A neural-network classifier of flaws for multifrequency Eddy-current tests of heat-exchange pipes
Source abstract
Abstract
A neural-network classifier has been developed that evaluates the geometric characteristics of a detected flaw on the basis of the parameters of the corresponding multifrequency signals obtained via scheduled eddy-current tests conducted with a through-type probe. The classifier is intended for testing heat-exchange pipes in steam generators of a nuclear power plant with a water-moderated water-cooled power reactor. The representative library of signals required for the design was formed on the basis of theoretical and experimental data. The theoretical data were obtained in a numerical physical and mathematical model of the electromagnetic testing procedure implemented with the MagNum3D program for finite-element analysis. The experimental data were obtained through measurement of multifrequency eddy-current signals from test specimens with artificial flaws.
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Research context
OpenAlex topics: Non-Destructive Testing Techniques · Ultrasonics and Acoustic Wave Propagation · Material Properties and Failure Mechanisms
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (12)
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