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Metal magnetic memory (MMM) signals can reflect stress concentration and cracks on the surface of
ferromagnetic components, but the traditional criteria used to distinguish the locations of these stress concentrations and
cracks are not sufficiently accurate. In this study, 22 indices were extracted from the original MMM signals, and the
diagnosis results of 4 kernel functions of support vector machine (SVM) were compared. Of these 4, the radial basis
function (RBF) kernel performed the best in the simulations, with a diagnostic accuracy of 94.03%. Using the principles
of adaptive genetic algorithms (AGA), a combined AGA-SVM diagnosis model was created, resulting in an improvement
in accuracy to 95.52%, using the same training and test sets as those used in the simulation of SVM with an RBF kernel.
The results show that AGA-SVM can accurately distinguish stress concentrations and cracks from normal points, enabling
them to be located more accurately.